Packages and custom functions

The lnRR_func function is here used to calculate a log response ratio (lnRR) adjusted for small sample sizes. In addition, this formula accounts for correlated samples. For more details, see Doncaster and Spake (2018) Correction for bias in meta-analysis of little-replicated studies. Methods in Ecology and Evolution; 9:634-644

# packages
library(tidyverse)
library(googlesheets4)
library(here)
library(metafor) 
library(metaAidR) # see a note above
library(orchaRd) # see a note above
library(ape)
library(clubSandwich)
library(metaAidR)
library(patchwork)
library(emmeans) # see a note above
library(kableExtra)
library(GGally)
library(cowplot)

# Below is the custom function to calculate the lnRR 
lnRR_func <- function(Mc, Nc, Me, Ne, aCV2c, aCV2e, rho = 0.5){
  lnRR <- log(Me/Mc) + 
        0.5 * ((aCV2e/Ne) - (aCV2c/Nc)) 
  
  var_lnRR <- (aCV2c/Nc) + (aCV2e/Ne) - 
         2*rho*(sqrt(aCV2c)*sqrt(aCV2e)/(Nc)) 
  
  data.frame(lnRR,var_lnRR)
}

# Mc: Concentration of PFAS of the raw (control) sample
# Nc: Sample size of the raw (control) sample
# Me: Concentration of PFAS of the cooked (experimental) sample
# Ne: Sample size of the cooked (experimental) sample 
# aCV2c: Mean coefficient of variation of the raw (control) samples
# aCV2e: Mean coefficient of variation of the cooked (experimental) samples

Data import and processing

Import and process raw data

Import raw data

raw_data <- read_sheet("https://docs.google.com/spreadsheets/d/1cbmYDfIc2dxHJxBaowojUZZkN31NW4sL_pHw0t9eTTU/edit#gid=477880397", 
    range = "Data_extraction_2", skip = 1, col_types = "ccncccccncncccccnncccnccnncncnccnnncncncccccccc")  # Import raw data

Process raw data

processed_data <- filter(raw_data, !PFAS_type == "PFOS_Total")
processed_data <- filter(processed_data, !Species_common == "Fish cake")

write.csv(processed_data, here("data", "pilot_data_preprocessed.csv"), row.names = F)

Load processed data

processed_data <- read_csv(here("data", "pilot_data_preprocessed.csv")) 

dat <- processed_data %>% mutate(SDc = ifelse(Sc_technical_biological == "biological", Sc, NA), # Calculate the SD of biological replicates for control samples
                                 SDe = ifelse(Se_technical_biological == "biological", Se, NA)) # Calculate the SD of biological replicates for experimental samples



kable(dat, "html") %>% kable_styling("striped", position = "left") %>% scroll_box(width = "100%", height = "500px")
Study_ID Author_year Publication_year Country_firstAuthor Effect_ID Species_common Species_Scientific Invertebrate_vertebrate Fish_mollusc Moisture_loss_in_percent PFAS_type PFAS_carbon_chain linear_total Choice_of_9 Cooking_method Cooking_Category Comments_cooking Temperature_in_Celsius Length_cooking_time_in_s Water Oil Oil_type Volume_liquid_ml Cohort_ID Cohort_comment Nc Pooled_Nc Unit_PFAS_conc Mc Mc_comment Sc sd Sc_technical_biological Ne Pooled_Ne Me Me_comment Se Se_technical_biological If_technical_how_many Unit_LOD_LOQ LOD LOQ Design DataSource Raw_data_provided General_comments checked SDc SDe
F001 Alves_2017 2017 Portugal E001 Flounder Platichthys flesus vertebrate marine fish 7.430000 PFOS 8 linear Yes Steaming water-based NA 105 900 Yes No NA NA C001 NA 25 1 ng/g 24.0000000 NA 1.5280000 sd technical 25 1 22.0000000 NA 1.5300000 technical 2 ng/g <0.1 <0.2 Dependent Table 3 No Authors replied ML - ok NA NA
F001 Alves_2017 2017 Portugal E002 Mackerel Scomber scombrus vertebrate marine fish NA PFUnDA 11 NA Yes Steaming water-based NA 105 900 Yes No NA NA C002 NA 25 1 ng/g 3.1000000 NA 0.2120000 sd technical 25 1 2.9000000 NA 0.1410000 technical 2 ng/g <0.1 <0.2 Dependent Table 3 No Authors replied ML - ok NA NA
F002 Barbosa_2018 2018 Portugal E003 Skipjack tuna Katsuwonus pelamis vertebrate marine fish 16.860000 PFUnDA 11 NA Yes Steaming water-based wrapped up in aluminum foil 105 900 Yes No NA NA C003 NA 25 1 ng/g 13.3018868 NA 0.0471698 sd technical 25 1 4.1509434 NA 0.0943396 technical 2 ng/g <0.01 <0.04 Dependent Table 2 Yes Authors replied NA NA NA
F002 Barbosa_2018 2018 Portugal E004 Skipjack tuna Katsuwonus pelamis vertebrate marine fish 16.860000 PFDoDA 12 NA No Steaming water-based wrapped up in aluminum foil 105 900 Yes No NA NA C003 NA 25 1 ng/g 3.5731707 NA 0.0243902 sd technical 25 1 3.2073171 NA 0.0243902 technical 2 ng/g <0.01 <0.04 Dependent Table 2 Yes Authors replied NA NA NA
F002 Barbosa_2018 2018 Portugal E005 Skipjack tuna Katsuwonus pelamis vertebrate marine fish 16.860000 PFTrA 13 NA No Steaming water-based wrapped up in aluminum foil 105 900 Yes No NA NA C003 NA 25 1 ng/g 6.5283019 NA 0.0754717 sd technical 25 1 10.0377358 NA 0.0754717 technical 2 ng/g <0.01 <0.04 Dependent Table 2 Yes Authors replied NA NA NA
F002 Barbosa_2018 2018 Portugal E006 Skipjack tuna Katsuwonus pelamis vertebrate marine fish 16.860000 PFTA 14 NA No Steaming water-based wrapped up in aluminum foil 105 900 Yes No NA NA C003 NA 25 1 ng/g 1.3736842 NA 0.0157895 sd technical 25 1 1.3315789 NA 0.0210526 technical 2 ng/g <0.01 <0.04 Dependent Table 2 Yes Authors replied NA NA NA
F002 Barbosa_2018 2018 Portugal E007 Skipjack tuna Katsuwonus pelamis vertebrate marine fish 16.860000 PFOS 8 total Yes Steaming water-based wrapped up in aluminum foil 105 900 Yes No NA NA C003 NA 25 1 ng/g 0.6467391 NA 0.0054348 sd technical 25 1 0.3016304 NA 0.0081522 technical 2 ng/g <0.01 <0.04 Dependent Table 2 Yes Authors replied NA NA NA
F002 Barbosa_2018 2018 Portugal E008 Skipjack tuna Katsuwonus pelamis vertebrate marine fish 16.860000 PFDA 10 NA Yes Steaming water-based wrapped up in aluminum foil 105 900 Yes No NA NA C003 NA 25 1 ng/g 0.0250000 <LOQ NA sd technical 25 1 0.0869767 NA 0.0130233 technical 2 ng/g <0.01 <0.04 Dependent Table 2 Yes Authors replied NA NA NA
F002 Barbosa_2018 2018 Portugal E009 European plaice Pleuronectes platessa vertebrate marine fish 8.700000 PFOS 8 total Yes Steaming water-based wrapped up in aluminum foil 105 900 Yes No NA NA C004 NA 25 1 ng/g 0.2472826 NA 0.0081522 sd technical 25 1 0.2527174 NA 0.0054348 technical 2 ng/g <0.01 <0.04 Dependent Table 2 Yes Authors replied NA NA NA
F002 Barbosa_2018 2018 Portugal E010 blue mussel Mytilus edulis invertebrate mollusca 6.770000 PFBA 3 NA No Steaming water-based wrapped up in aluminum foil 105 900 Yes No NA NA C005 NA 50 1 ng/g 0.0250000 <LOQ NA sd technical 50 1 0.2083333 NA 0.0090909 technical 2 ng/g <0.01 <0.04 Dependent Table 2 Yes Authors replied NA NA NA
F002 Barbosa_2018 2018 Portugal E011 blue mussel Mytilus edulis invertebrate mollusca 6.770000 PFDA 10 NA Yes Steaming water-based wrapped up in aluminum foil 105 900 Yes No NA NA C005 NA 50 1 ng/g 0.0241860 NA 0.0074419 sd technical 50 1 0.0250000 <LOQ NA technical 2 ng/g <0.01 <0.04 Dependent Table 2 Yes Authors replied NA NA NA
F003 Bhavsar_2014 2014 Canada E012 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 14.400000 PFNA 9 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C006 NA 5 5 ng/g 0.0670000 NA 0.0950000 sd biological 5 5 0.0860000 NA 0.1350000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. ML 0.0950000 0.1350000
F003 Bhavsar_2014 2014 Canada E013 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 14.400000 PFDA 10 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11 C006 NA 5 5 ng/g 0.1560000 NA 0.1970000 sd biological 5 5 0.1920000 NA 0.2660000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1970000 0.2660000
F003 Bhavsar_2014 2014 Canada E014 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 14.400000 PFUnDA 11 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11 C006 NA 5 5 ng/g 0.1860000 NA 0.2250000 sd biological 5 5 0.2340000 NA 0.2910000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.2250000 0.2910000
F003 Bhavsar_2014 2014 Canada E015 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 14.400000 PFDoDA 12 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C006 NA 5 5 ng/g 0.0800000 NA 0.0730000 sd biological 5 5 0.1010000 NA 0.0950000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0730000 0.0950000
F003 Bhavsar_2014 2014 Canada E016 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 14.400000 PFTrA 13 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C006 NA 5 5 ng/g 0.2150000 NA 0.1830000 sd biological 5 5 0.2590000 NA 0.2410000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1830000 0.2410000
F003 Bhavsar_2014 2014 Canada E017 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 14.400000 PFTA 14 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C006 NA 5 5 ng/g 0.0760000 NA 0.0550000 sd biological 5 5 0.0830000 NA 0.0730000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0550000 0.0730000
F003 Bhavsar_2014 2014 Canada E019 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 14.400000 PFOS 8 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11 C006 NA 5 5 ng/g 12.7000000 NA 12.6100000 sd biological 5 5 16.5600000 NA 18.0000000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 12.6100000 18.0000000
F003 Bhavsar_2014 2014 Canada E020 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 14.400000 PFDS 10 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C006 NA 5 5 ng/g 0.3030000 NA 0.2840000 sd biological 5 5 0.3970000 NA 0.4330000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.2840000 0.4330000
F003 Bhavsar_2014 2014 Canada E021 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 14.400000 6:6PFPIA 12 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C006 NA 5 5 ng/g 0.0030000 NA 0.0030000 sd biological 5 5 0.0020000 NA 0.0020000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0030000 0.0020000
F003 Bhavsar_2014 2014 Canada E022 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 14.400000 6:8PFPIA 14 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C006 NA 5 5 ng/g 0.0170000 NA 0.0230000 sd biological 5 5 0.0100000 NA 0.0160000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0230000 0.0160000
F003 Bhavsar_2014 2014 Canada E023 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 19.680000 PFNA 9 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C007 NA 5 5 ng/g 0.0670000 NA 0.0950000 sd biological 5 5 0.0830000 NA 0.1180000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0950000 0.1180000
F003 Bhavsar_2014 2014 Canada E024 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 19.680000 PFDA 10 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 11 C007 NA 5 5 ng/g 0.1560000 NA 0.1970000 sd biological 5 5 0.1900000 NA 0.2320000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1970000 0.2320000
F003 Bhavsar_2014 2014 Canada E025 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 19.680000 PFUnDA 11 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 11 C007 NA 5 5 ng/g 0.1860000 NA 0.2250000 sd biological 5 5 0.2560000 NA 0.3100000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.2250000 0.3100000
F003 Bhavsar_2014 2014 Canada E026 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 19.680000 PFDoDA 12 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C007 NA 5 5 ng/g 0.0800000 NA 0.0730000 sd biological 5 5 0.1000000 NA 0.0800000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0730000 0.0800000
F003 Bhavsar_2014 2014 Canada E027 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 19.680000 PFTrA 13 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C007 NA 5 5 ng/g 0.2150000 NA 0.1830000 sd biological 5 5 0.2850000 NA 0.2340000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1830000 0.2340000
F003 Bhavsar_2014 2014 Canada E028 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 19.680000 PFTA 14 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C007 NA 5 5 ng/g 0.0760000 NA 0.0550000 sd biological 5 5 0.0830000 NA 0.0710000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0550000 0.0710000
F003 Bhavsar_2014 2014 Canada E030 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 19.680000 PFOS 8 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 11 C007 NA 5 5 ng/g 12.7000000 NA 12.6100000 sd biological 5 5 16.4500000 NA 15.6300000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 12.6100000 15.6300000
F003 Bhavsar_2014 2014 Canada E031 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 19.680000 PFDS 10 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C007 NA 5 5 ng/g 0.3030000 NA 0.2840000 sd biological 5 5 0.3920000 NA 0.3590000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.2840000 0.3590000
F003 Bhavsar_2014 2014 Canada E032 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 19.680000 6:6PFPIA 12 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C007 NA 5 5 ng/g 0.0030000 NA 0.0030000 sd biological 5 5 0.0020000 NA 0.0030000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0030000 0.0030000
F003 Bhavsar_2014 2014 Canada E033 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 19.680000 6:8PFPIA 14 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C007 NA 5 5 ng/g 0.0170000 NA 0.0230000 sd biological 5 5 0.0140000 NA 0.0220000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0230000 0.0220000
F003 Bhavsar_2014 2014 Canada E034 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 18.680000 PFNA 9 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C008 NA 5 5 ng/g 0.0670000 NA 0.0950000 sd biological 5 5 0.0780000 NA 0.1140000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0950000 0.1140000
F003 Bhavsar_2014 2014 Canada E035 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 18.680000 PFDA 10 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11 C008 NA 5 5 ng/g 0.1560000 NA 0.1970000 sd biological 5 5 0.1820000 NA 0.2220000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1970000 0.2220000
F003 Bhavsar_2014 2014 Canada E036 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 18.680000 PFUnDA 11 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11 C008 NA 5 5 ng/g 0.1860000 NA 0.2250000 sd biological 5 5 0.2270000 NA 0.2550000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.2250000 0.2550000
F003 Bhavsar_2014 2014 Canada E037 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 18.680000 PFDoDA 12 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C008 NA 5 5 ng/g 0.0800000 NA 0.0730000 sd biological 5 5 0.0960000 NA 0.0810000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0730000 0.0810000
F003 Bhavsar_2014 2014 Canada E038 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 18.680000 PFTrA 13 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C008 NA 5 5 ng/g 0.2150000 NA 0.1830000 sd biological 5 5 0.2750000 NA 0.2160000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1830000 0.2160000
F003 Bhavsar_2014 2014 Canada E039 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 18.680000 PFTA 14 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C008 NA 5 5 ng/g 0.0760000 NA 0.0550000 sd biological 5 5 0.0870000 NA 0.0670000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0550000 0.0670000
F003 Bhavsar_2014 2014 Canada E041 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 18.680000 PFOS 8 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11 C008 NA 5 5 ng/g 12.7000000 NA 12.6100000 sd biological 5 5 16.0300000 NA 15.1900000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 12.6100000 15.1900000
F003 Bhavsar_2014 2014 Canada E042 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 18.680000 PFDS 10 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C008 NA 5 5 ng/g 0.3030000 NA 0.2840000 sd biological 5 5 0.3930000 NA 0.3690000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.2840000 0.3690000
F003 Bhavsar_2014 2014 Canada E043 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 18.680000 6:6PFPIA 12 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C008 NA 5 5 ng/g 0.0030000 NA 0.0030000 sd biological 5 5 0.0020000 NA 0.0030000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0030000 0.0030000
F003 Bhavsar_2014 2014 Canada E044 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 18.680000 6:8PFPIA 14 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C008 NA 5 5 ng/g 0.0170000 NA 0.0230000 sd biological 5 5 0.0130000 NA 0.0220000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0230000 0.0220000
F003 Bhavsar_2014 2014 Canada E045 Common carp Cyprinus carpio vertebrate freshwater fish 15.470000 PFNA 9 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C009 NA 5 5 ng/g 0.0920000 NA 0.0300000 sd biological 5 5 0.0990000 NA 0.0220000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0300000 0.0220000
F003 Bhavsar_2014 2014 Canada E046 Common carp Cyprinus carpio vertebrate freshwater fish 15.470000 PFDA 10 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11 C009 NA 5 5 ng/g 0.5180000 NA 0.1070000 sd biological 5 5 0.5660000 NA 0.1380000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1070000 0.1380000
F003 Bhavsar_2014 2014 Canada E047 Common carp Cyprinus carpio vertebrate freshwater fish 15.470000 PFUnDA 11 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11 C009 NA 5 5 ng/g 0.7120000 NA 0.1580000 sd biological 5 5 0.8040000 NA 0.1670000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1580000 0.1670000
F003 Bhavsar_2014 2014 Canada E048 Common carp Cyprinus carpio vertebrate freshwater fish 15.470000 PFDoDA 12 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C009 NA 5 5 ng/g 0.9890000 NA 0.3170000 sd biological 5 5 1.0960000 NA 0.3960000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.3170000 0.3960000
F003 Bhavsar_2014 2014 Canada E049 Common carp Cyprinus carpio vertebrate freshwater fish 15.470000 PFTrA 13 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C009 NA 5 5 ng/g 0.7790000 NA 0.4400000 sd biological 5 5 0.7740000 NA 0.3320000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.4400000 0.3320000
F003 Bhavsar_2014 2014 Canada E050 Common carp Cyprinus carpio vertebrate freshwater fish 15.470000 PFTA 14 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C009 NA 5 5 ng/g 0.9510000 NA 0.6470000 sd biological 5 5 1.1400000 NA 0.8740000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.6470000 0.8740000
F003 Bhavsar_2014 2014 Canada E051 Common carp Cyprinus carpio vertebrate freshwater fish 15.470000 PFHxS 6 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11 C009 NA 5 5 ng/g 0.2920000 NA 0.3190000 sd biological 5 5 0.3410000 NA 0.3910000 biological NA ng/g Probably <0.006 Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.3190000 0.3910000
F003 Bhavsar_2014 2014 Canada E052 Common carp Cyprinus carpio vertebrate freshwater fish 15.470000 PFOS 8 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11 C009 NA 5 5 ng/g 27.1700000 NA 7.7680000 sd biological 5 5 30.5200000 NA 9.2540000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 7.7680000 9.2540000
F003 Bhavsar_2014 2014 Canada E053 Common carp Cyprinus carpio vertebrate freshwater fish 15.470000 PFDS 10 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C009 NA 5 5 ng/g 0.9110000 NA 0.5320000 sd biological 5 5 1.0840000 NA 0.5710000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.5320000 0.5710000
F003 Bhavsar_2014 2014 Canada E054 Common carp Cyprinus carpio vertebrate freshwater fish 15.470000 6:6PFPIA 12 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C009 NA 5 5 ng/g 0.0980000 NA 0.0600000 sd biological 5 5 0.1050000 NA 0.0600000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0600000 0.0600000
F003 Bhavsar_2014 2014 Canada E055 Common carp Cyprinus carpio vertebrate freshwater fish 15.470000 6:8PFPIA 14 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C010 NA 5 5 ng/g 0.1670000 NA 0.0770000 sd biological 5 5 0.1800000 NA 0.0840000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0770000 0.0840000
F003 Bhavsar_2014 2014 Canada E056 Common carp Cyprinus carpio vertebrate freshwater fish 19.680000 PFNA 9 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C010 NA 5 5 ng/g 0.0920000 NA 0.0300000 sd biological 5 5 0.1050000 NA 0.0370000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0300000 0.0370000
F003 Bhavsar_2014 2014 Canada E057 Common carp Cyprinus carpio vertebrate freshwater fish 19.680000 PFDA 10 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 11 C010 NA 5 5 ng/g 0.5180000 NA 0.1070000 sd biological 5 5 0.5480000 NA 0.1210000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1070000 0.1210000
F003 Bhavsar_2014 2014 Canada E058 Common carp Cyprinus carpio vertebrate freshwater fish 19.680000 PFUnDA 11 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 11 C010 NA 5 5 ng/g 0.7120000 NA 0.1580000 sd biological 5 5 0.8480000 NA 0.1550000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1580000 0.1550000
F003 Bhavsar_2014 2014 Canada E059 Common carp Cyprinus carpio vertebrate freshwater fish 19.680000 PFDoDA 12 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C010 NA 5 5 ng/g 0.9890000 NA 0.3170000 sd biological 5 5 1.1080000 NA 0.4040000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.3170000 0.4040000
F003 Bhavsar_2014 2014 Canada E060 Common carp Cyprinus carpio vertebrate freshwater fish 19.680000 PFTrA 13 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C010 NA 5 5 ng/g 0.7790000 NA 0.4400000 sd biological 5 5 0.8280000 NA 0.4180000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.4400000 0.4180000
F003 Bhavsar_2014 2014 Canada E061 Common carp Cyprinus carpio vertebrate freshwater fish 19.680000 PFTA 14 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C010 NA 5 5 ng/g 0.9510000 NA 0.6470000 sd biological 5 5 1.1150000 NA 0.7690000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.6470000 0.7690000
F003 Bhavsar_2014 2014 Canada E062 Common carp Cyprinus carpio vertebrate freshwater fish 19.680000 PFHxS 6 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 11 C010 NA 5 5 ng/g 0.2920000 NA 0.3190000 sd biological 5 5 0.2910000 NA 0.3460000 biological NA ng/g Probably <0.006 Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.3190000 0.3460000
F003 Bhavsar_2014 2014 Canada E063 Common carp Cyprinus carpio vertebrate freshwater fish 19.680000 PFOS 8 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 11 C010 NA 5 5 ng/g 27.1700000 NA 7.7680000 sd biological 5 5 28.3700000 NA 11.9900000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 7.7680000 11.9900000
F003 Bhavsar_2014 2014 Canada E064 Common carp Cyprinus carpio vertebrate freshwater fish 19.680000 PFDS 10 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C010 NA 5 5 ng/g 0.9110000 NA 0.5320000 sd biological 5 5 1.0450000 NA 0.6230000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.5320000 0.6230000
F003 Bhavsar_2014 2014 Canada E065 Common carp Cyprinus carpio vertebrate freshwater fish 19.680000 6:6PFPIA 12 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C010 NA 5 5 ng/g 0.0980000 NA 0.0600000 sd biological 5 5 0.1170000 NA 0.0730000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0600000 0.0730000
F003 Bhavsar_2014 2014 Canada E066 Common carp Cyprinus carpio vertebrate freshwater fish 19.680000 6:8PFPIA 14 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C010 NA 5 5 ng/g 0.1670000 NA 0.0770000 sd biological 5 5 0.1900000 NA 0.0800000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0770000 0.0800000
F003 Bhavsar_2014 2014 Canada E067 Common carp Cyprinus carpio vertebrate freshwater fish 14.910000 PFNA 9 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C011 NA 5 5 ng/g 0.0920000 NA 0.0300000 sd biological 5 5 0.1010000 NA 0.0350000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0300000 0.0350000
F003 Bhavsar_2014 2014 Canada E068 Common carp Cyprinus carpio vertebrate freshwater fish 14.910000 PFDA 10 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11 C011 NA 5 5 ng/g 0.5180000 NA 0.1070000 sd biological 5 5 0.5690000 NA 0.1080000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1070000 0.1080000
F003 Bhavsar_2014 2014 Canada E069 Common carp Cyprinus carpio vertebrate freshwater fish 14.910000 PFUnDA 11 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11 C011 NA 5 5 ng/g 0.7120000 NA 0.1580000 sd biological 5 5 0.8300000 NA 0.1300000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1580000 0.1300000
F003 Bhavsar_2014 2014 Canada E070 Common carp Cyprinus carpio vertebrate freshwater fish 14.910000 PFDoDA 12 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C011 NA 5 5 ng/g 0.9890000 NA 0.3170000 sd biological 5 5 1.0440000 NA 0.3560000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.3170000 0.3560000
F003 Bhavsar_2014 2014 Canada E071 Common carp Cyprinus carpio vertebrate freshwater fish 14.910000 PFTrA 13 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C011 NA 5 5 ng/g 0.7790000 NA 0.4400000 sd biological 5 5 0.7460000 NA 0.2830000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.4400000 0.2830000
F003 Bhavsar_2014 2014 Canada E072 Common carp Cyprinus carpio vertebrate freshwater fish 14.910000 PFTA 14 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C011 NA 5 5 ng/g 0.9510000 NA 0.6470000 sd biological 5 5 1.0670000 NA 0.7540000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.6470000 0.7540000
F003 Bhavsar_2014 2014 Canada E073 Common carp Cyprinus carpio vertebrate freshwater fish 14.910000 PFHxS 6 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11 C011 NA 5 5 ng/g 0.2920000 NA 0.3190000 sd biological 5 5 0.3590000 NA 0.4280000 biological NA ng/g Probably <0.006 Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.3190000 0.4280000
F003 Bhavsar_2014 2014 Canada E074 Common carp Cyprinus carpio vertebrate freshwater fish 14.910000 PFOS 8 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11 C011 NA 5 5 ng/g 27.1700000 NA 7.7680000 sd biological 5 5 28.1100000 NA 10.9300000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 7.7680000 10.9300000
F003 Bhavsar_2014 2014 Canada E075 Common carp Cyprinus carpio vertebrate freshwater fish 14.910000 PFDS 10 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C011 NA 5 5 ng/g 0.9110000 NA 0.5320000 sd biological 5 5 1.0900000 NA 0.6180000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.5320000 0.6180000
F003 Bhavsar_2014 2014 Canada E076 Common carp Cyprinus carpio vertebrate freshwater fish 14.910000 6:6PFPIA 12 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C011 NA 5 5 ng/g 0.0980000 NA 0.0600000 sd biological 5 5 0.1060000 NA 0.0650000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0600000 0.0650000
F003 Bhavsar_2014 2014 Canada E077 Common carp Cyprinus carpio vertebrate freshwater fish 14.910000 6:8PFPIA 14 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C011 NA 5 5 ng/g 0.1670000 NA 0.0770000 sd biological 5 5 0.1880000 NA 0.0750000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0770000 0.0750000
F003 Bhavsar_2014 2014 Canada E078 Lake trout Salvelinus namaycush vertebrate freshwater fish 10.130000 PFNA 9 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C012 NA 4 4 ng/g 0.2980000 NA 0.1430000 sd biological 4 5 0.3700000 NA 0.1890000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1430000 0.1890000
F003 Bhavsar_2014 2014 Canada E079 Lake trout Salvelinus namaycush vertebrate freshwater fish 10.130000 PFDA 10 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11 C012 NA 4 4 ng/g 0.4230000 NA 0.1860000 sd biological 4 5 0.5100000 NA 0.2320000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1860000 0.2320000
F003 Bhavsar_2014 2014 Canada E080 Lake trout Salvelinus namaycush vertebrate freshwater fish 10.130000 PFUnDA 11 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11 C012 NA 4 4 ng/g 0.5600000 NA 0.2510000 sd biological 4 5 0.6850000 NA 0.2930000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.2510000 0.2930000
F003 Bhavsar_2014 2014 Canada E081 Lake trout Salvelinus namaycush vertebrate freshwater fish 10.130000 PFDoDA 12 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C012 NA 4 4 ng/g 0.1980000 NA 0.0950000 sd biological 4 5 0.2210000 NA 0.1140000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0950000 0.1140000
F003 Bhavsar_2014 2014 Canada E082 Lake trout Salvelinus namaycush vertebrate freshwater fish 10.130000 PFTrA 13 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C012 NA 4 4 ng/g 0.4610000 NA 0.2170000 sd biological 4 5 0.4840000 NA 0.2640000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.2170000 0.2640000
F003 Bhavsar_2014 2014 Canada E083 Lake trout Salvelinus namaycush vertebrate freshwater fish 10.130000 PFTA 14 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C012 NA 4 4 ng/g 0.1280000 NA 0.0510000 sd biological 4 5 0.1370000 NA 0.0510000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0510000 0.0510000
F003 Bhavsar_2014 2014 Canada E084 Lake trout Salvelinus namaycush vertebrate freshwater fish 10.130000 PFHxS 6 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11 C012 NA 4 4 ng/g 0.2580000 NA 0.0550000 sd biological 4 5 0.2480000 NA 0.0610000 biological NA ng/g Probably <0.006 Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0550000 0.0610000
F003 Bhavsar_2014 2014 Canada E085 Lake trout Salvelinus namaycush vertebrate freshwater fish 10.130000 PFOS 8 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11 C012 NA 4 4 ng/g 18.1800000 NA 6.6860000 sd biological 4 5 20.5100000 NA 6.7520000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 6.6860000 6.7520000
F003 Bhavsar_2014 2014 Canada E086 Lake trout Salvelinus namaycush vertebrate freshwater fish 10.130000 PFDS 10 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C012 NA 4 4 ng/g 0.4560000 NA 0.1770000 sd biological 4 5 0.4740000 NA 0.1960000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1770000 0.1960000
F003 Bhavsar_2014 2014 Canada E087 Lake trout Salvelinus namaycush vertebrate freshwater fish 10.130000 6:6PFPIA 12 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C012 NA 4 4 ng/g 0.0020000 NA 0.0010000 sd biological 4 5 0.0020000 NA 0.0020000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0010000 0.0020000
F003 Bhavsar_2014 2014 Canada E088 Lake trout Salvelinus namaycush vertebrate freshwater fish 10.130000 6:8PFPIA 14 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C012 NA 4 4 ng/g 0.0170000 NA 0.0090000 sd biological 4 5 0.0180000 NA 0.0090000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0090000 0.0090000
F003 Bhavsar_2014 2014 Canada E089 Lake trout Salvelinus namaycush vertebrate freshwater fish 15.230000 PFNA 9 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C013 NA 4 4 ng/g 0.2980000 NA 0.1430000 sd biological 4 5 0.3580000 NA 0.1700000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1430000 0.1700000
F003 Bhavsar_2014 2014 Canada E090 Lake trout Salvelinus namaycush vertebrate freshwater fish 15.230000 PFDA 10 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 11 C013 NA 4 4 ng/g 0.4230000 NA 0.1860000 sd biological 4 5 0.5280000 NA 0.2330000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1860000 0.2330000
F003 Bhavsar_2014 2014 Canada E091 Lake trout Salvelinus namaycush vertebrate freshwater fish 15.230000 PFUnDA 11 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 11 C013 NA 4 4 ng/g 0.5600000 NA 0.2510000 sd biological 4 5 0.7250000 NA 0.3450000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.2510000 0.3450000
F003 Bhavsar_2014 2014 Canada E092 Lake trout Salvelinus namaycush vertebrate freshwater fish 15.230000 PFDoDA 12 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C013 NA 4 4 ng/g 0.1980000 NA 0.0950000 sd biological 4 5 0.2370000 NA 0.1110000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0950000 0.1110000
F003 Bhavsar_2014 2014 Canada E093 Lake trout Salvelinus namaycush vertebrate freshwater fish 15.230000 PFTrA 13 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C013 NA 4 4 ng/g 0.4610000 NA 0.2170000 sd biological 4 5 0.5580000 NA 0.2800000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.2170000 0.2800000
F003 Bhavsar_2014 2014 Canada E094 Lake trout Salvelinus namaycush vertebrate freshwater fish 15.230000 PFTA 14 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C013 NA 4 4 ng/g 0.1280000 NA 0.0510000 sd biological 4 5 0.1490000 NA 0.0680000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0510000 0.0680000
F003 Bhavsar_2014 2014 Canada E095 Lake trout Salvelinus namaycush vertebrate freshwater fish 15.230000 PFHxS 6 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 11 C013 NA 4 4 ng/g 0.2580000 NA 0.0550000 sd biological 4 5 0.2630000 NA 0.0870000 biological NA ng/g Probably <0.006 Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0550000 0.0870000
F003 Bhavsar_2014 2014 Canada E096 Lake trout Salvelinus namaycush vertebrate freshwater fish 15.230000 PFOS 8 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 11 C013 NA 4 4 ng/g 18.1800000 NA 6.6860000 sd biological 4 5 22.1100000 NA 7.8970000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 6.6860000 7.8970000
F003 Bhavsar_2014 2014 Canada E097 Lake trout Salvelinus namaycush vertebrate freshwater fish 15.230000 PFDS 10 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C013 NA 4 4 ng/g 0.4560000 NA 0.1770000 sd biological 4 5 0.5600000 NA 0.2260000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1770000 0.2260000
F003 Bhavsar_2014 2014 Canada E098 Lake trout Salvelinus namaycush vertebrate freshwater fish 15.230000 6:6PFPIA 12 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C013 NA 4 4 ng/g 0.0020000 NA 0.0010000 sd biological 4 5 0.0120000 NA 0.0180000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0010000 0.0180000
F003 Bhavsar_2014 2014 Canada E099 Lake trout Salvelinus namaycush vertebrate freshwater fish 15.230000 6:8PFPIA 14 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C013 NA 4 4 ng/g 0.0170000 NA 0.0090000 sd biological 4 5 0.0160000 NA 0.0060000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0090000 0.0060000
F003 Bhavsar_2014 2014 Canada E100 Lake trout Salvelinus namaycush vertebrate freshwater fish 11.530000 PFNA 9 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C014 NA 4 4 ng/g 0.2980000 NA 0.1430000 sd biological 4 5 0.3740000 NA 0.1810000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1430000 0.1810000
F003 Bhavsar_2014 2014 Canada E101 Lake trout Salvelinus namaycush vertebrate freshwater fish 11.530000 PFDA 10 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11 C014 NA 4 4 ng/g 0.4230000 NA 0.1860000 sd biological 4 5 0.4930000 NA 0.2070000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1860000 0.2070000
F003 Bhavsar_2014 2014 Canada E102 Lake trout Salvelinus namaycush vertebrate freshwater fish 11.530000 PFUnDA 11 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11 C014 NA 4 4 ng/g 0.5600000 NA 0.2510000 sd biological 4 5 0.6830000 NA 0.2860000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.2510000 0.2860000
F003 Bhavsar_2014 2014 Canada E103 Lake trout Salvelinus namaycush vertebrate freshwater fish 11.530000 PFDoDA 12 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C014 NA 4 4 ng/g 0.1980000 NA 0.0950000 sd biological 4 5 0.2320000 NA 0.1030000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0950000 0.1030000
F003 Bhavsar_2014 2014 Canada E104 Lake trout Salvelinus namaycush vertebrate freshwater fish 11.530000 PFTrA 13 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C014 NA 4 4 ng/g 0.4610000 NA 0.2170000 sd biological 4 5 0.5190000 NA 0.2120000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.2170000 0.2120000
F003 Bhavsar_2014 2014 Canada E105 Lake trout Salvelinus namaycush vertebrate freshwater fish 11.530000 PFTA 14 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C014 NA 4 4 ng/g 0.1280000 NA 0.0510000 sd biological 4 5 0.1290000 NA 0.0450000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0510000 0.0450000
F003 Bhavsar_2014 2014 Canada E106 Lake trout Salvelinus namaycush vertebrate freshwater fish 11.530000 PFHxS 6 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11 C014 NA 4 4 ng/g 0.2580000 NA 0.0550000 sd biological 4 5 0.2450000 NA 0.0770000 biological NA ng/g Probably <0.006 Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0550000 0.0770000
F003 Bhavsar_2014 2014 Canada E107 Lake trout Salvelinus namaycush vertebrate freshwater fish 11.530000 PFOS 8 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11 C014 NA 4 4 ng/g 18.1800000 NA 6.6860000 sd biological 4 5 21.6700000 NA 8.0080000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 6.6860000 8.0080000
F003 Bhavsar_2014 2014 Canada E108 Lake trout Salvelinus namaycush vertebrate freshwater fish 11.530000 PFDS 10 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C014 NA 4 4 ng/g 0.4560000 NA 0.1770000 sd biological 4 5 0.5160000 NA 0.2440000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1770000 0.2440000
F003 Bhavsar_2014 2014 Canada E109 Lake trout Salvelinus namaycush vertebrate freshwater fish 11.530000 6:6PFPIA 12 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C014 NA 4 4 ng/g 0.0020000 NA 0.0010000 sd biological 4 5 0.0020000 NA 0.0010000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0010000 0.0010000
F003 Bhavsar_2014 2014 Canada E110 Lake trout Salvelinus namaycush vertebrate freshwater fish 11.530000 6:8PFPIA 14 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C014 NA 4 4 ng/g 0.0170000 NA 0.0090000 sd biological 4 5 0.0160000 NA 0.0060000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0090000 0.0060000
F003 Bhavsar_2014 2014 Canada E111 Walleye Sander vitreus vertebrate freshwater fish 18.710000 PFNA 9 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C014 NA 5 5 ng/g 0.0630000 NA 0.0210000 sd biological 5 5 0.0790000 NA 0.0230000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0210000 0.0230000
F003 Bhavsar_2014 2014 Canada E112 Walleye Sander vitreus vertebrate freshwater fish 18.710000 PFDA 10 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11 C014 NA 5 5 ng/g 0.2480000 NA 0.0400000 sd biological 5 5 0.3490000 NA 0.0940000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0400000 0.0940000
F003 Bhavsar_2014 2014 Canada E113 Walleye Sander vitreus vertebrate freshwater fish 18.710000 PFUnDA 11 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11 C014 NA 5 5 ng/g 0.2390000 NA 0.0400000 sd biological 5 5 0.3330000 NA 0.0910000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0400000 0.0910000
F003 Bhavsar_2014 2014 Canada E114 Walleye Sander vitreus vertebrate freshwater fish 18.710000 PFDoDA 12 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C014 NA 5 5 ng/g 0.1050000 NA 0.0190000 sd biological 5 5 0.1330000 NA 0.0120000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0190000 0.0120000
F003 Bhavsar_2014 2014 Canada E115 Walleye Sander vitreus vertebrate freshwater fish 18.710000 PFTrA 13 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C014 NA 5 5 ng/g 0.1490000 NA 0.0200000 sd biological 5 5 0.1800000 NA 0.0210000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0200000 0.0210000
F003 Bhavsar_2014 2014 Canada E116 Walleye Sander vitreus vertebrate freshwater fish 18.710000 PFTA 14 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C014 NA 5 5 ng/g 0.0690000 NA 0.0090000 sd biological 5 5 0.0930000 NA 0.0230000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0090000 0.0230000
F003 Bhavsar_2014 2014 Canada E117 Walleye Sander vitreus vertebrate freshwater fish 18.710000 PFHxS 6 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11 C014 NA 5 5 ng/g 0.0800000 NA 0.0250000 sd biological 5 5 0.0980000 NA 0.0340000 biological NA ng/g Probably <0.006 Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0250000 0.0340000
F003 Bhavsar_2014 2014 Canada E118 Walleye Sander vitreus vertebrate freshwater fish 18.710000 PFOS 8 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11 C014 NA 5 5 ng/g 36.7900000 NA 1.6240000 sd biological 5 5 45.0900000 NA 3.7090000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 1.6240000 3.7090000
F003 Bhavsar_2014 2014 Canada E119 Walleye Sander vitreus vertebrate freshwater fish 18.710000 PFDS 10 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C014 NA 5 5 ng/g 0.1060000 NA 0.0240000 sd biological 5 5 0.1780000 NA 0.0940000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0240000 0.0940000
F003 Bhavsar_2014 2014 Canada E120 Walleye Sander vitreus vertebrate freshwater fish 18.710000 6:6PFPIA 12 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C014 NA 5 5 ng/g 0.0260000 NA 0.0060000 sd biological 5 5 0.0350000 NA 0.0060000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0060000 0.0060000
F003 Bhavsar_2014 2014 Canada E121 Walleye Sander vitreus vertebrate freshwater fish 18.710000 6:8PFPIA 14 NA No Baking oil-based NA 200 900 No Yes canola oil 11 C014 NA 5 5 ng/g 0.0670000 NA 0.0100000 sd biological 5 5 0.0630000 NA 0.0170000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0100000 0.0170000
F003 Bhavsar_2014 2014 Canada E122 Walleye Sander vitreus vertebrate freshwater fish 24.090000 PFNA 9 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C015 NA 5 5 ng/g 0.0630000 NA 0.0210000 sd biological 5 5 0.0740000 NA 0.0140000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0210000 0.0140000
F003 Bhavsar_2014 2014 Canada E123 Walleye Sander vitreus vertebrate freshwater fish 24.090000 PFDA 10 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 11 C015 NA 5 5 ng/g 0.2480000 NA 0.0400000 sd biological 5 5 0.3380000 NA 0.0980000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0400000 0.0980000
F003 Bhavsar_2014 2014 Canada E124 Walleye Sander vitreus vertebrate freshwater fish 24.090000 PFUnDA 11 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 11 C015 NA 5 5 ng/g 0.2390000 NA 0.0400000 sd biological 5 5 0.3480000 NA 0.1020000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0400000 0.1020000
F003 Bhavsar_2014 2014 Canada E125 Walleye Sander vitreus vertebrate freshwater fish 24.090000 PFDoDA 12 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C015 NA 5 5 ng/g 0.1050000 NA 0.0190000 sd biological 5 5 0.1440000 NA 0.0370000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0190000 0.0370000
F003 Bhavsar_2014 2014 Canada E126 Walleye Sander vitreus vertebrate freshwater fish 24.090000 PFTrA 13 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C015 NA 5 5 ng/g 0.1490000 NA 0.0200000 sd biological 5 5 0.2170000 NA 0.0410000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0200000 0.0410000
F003 Bhavsar_2014 2014 Canada E127 Walleye Sander vitreus vertebrate freshwater fish 24.090000 PFTA 14 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C015 NA 5 5 ng/g 0.0690000 NA 0.0090000 sd biological 5 5 0.0940000 NA 0.0250000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0090000 0.0250000
F003 Bhavsar_2014 2014 Canada E128 Walleye Sander vitreus vertebrate freshwater fish 24.090000 PFHxS 6 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 11 C015 NA 5 5 ng/g 0.0800000 NA 0.0250000 sd biological 5 5 0.0880000 NA 0.0360000 biological NA ng/g Probably <0.006 Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0250000 0.0360000
F003 Bhavsar_2014 2014 Canada E129 Walleye Sander vitreus vertebrate freshwater fish 24.090000 PFOS 8 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 11 C015 NA 5 5 ng/g 36.7900000 NA 1.6240000 sd biological 5 5 52.6900000 NA 14.6200000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 1.6240000 14.6200000
F003 Bhavsar_2014 2014 Canada E130 Walleye Sander vitreus vertebrate freshwater fish 24.090000 PFDS 10 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C015 NA 5 5 ng/g 0.1060000 NA 0.0240000 sd biological 5 5 0.1890000 NA 0.0800000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0240000 0.0800000
F003 Bhavsar_2014 2014 Canada E131 Walleye Sander vitreus vertebrate freshwater fish 24.090000 6:6PFPIA 12 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C015 NA 5 5 ng/g 0.0260000 NA 0.0060000 sd biological 5 5 0.0400000 NA 0.0080000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0060000 0.0080000
F003 Bhavsar_2014 2014 Canada E132 Walleye Sander vitreus vertebrate freshwater fish 24.090000 6:8PFPIA 14 NA No Broiling oil-based NA 300 600 No Yes canola oil 11 C015 NA 5 5 ng/g 0.0670000 NA 0.0100000 sd biological 5 5 0.0870000 NA 0.0120000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0100000 0.0120000
F003 Bhavsar_2014 2014 Canada E133 Walleye Sander vitreus vertebrate freshwater fish 14.450000 PFNA 9 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C016 NA 5 5 ng/g 0.0630000 NA 0.0210000 sd biological 5 5 0.0670000 NA 0.0150000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0210000 0.0150000
F003 Bhavsar_2014 2014 Canada E134 Walleye Sander vitreus vertebrate freshwater fish 14.450000 PFDA 10 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11 C016 NA 5 5 ng/g 0.2480000 NA 0.0400000 sd biological 5 5 0.2990000 NA 0.0720000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0400000 0.0720000
F003 Bhavsar_2014 2014 Canada E135 Walleye Sander vitreus vertebrate freshwater fish 14.450000 PFUnDA 11 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11 C016 NA 5 5 ng/g 0.2390000 NA 0.0400000 sd biological 5 5 0.3070000 NA 0.0760000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0400000 0.0760000
F003 Bhavsar_2014 2014 Canada E136 Walleye Sander vitreus vertebrate freshwater fish 14.450000 PFDoDA 12 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C016 NA 5 5 ng/g 0.1050000 NA 0.0190000 sd biological 5 5 0.1290000 NA 0.0490000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0190000 0.0490000
F003 Bhavsar_2014 2014 Canada E137 Walleye Sander vitreus vertebrate freshwater fish 14.450000 PFTrA 13 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C016 NA 5 5 ng/g 0.1490000 NA 0.0200000 sd biological 5 5 0.1790000 NA 0.0540000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0200000 0.0540000
F003 Bhavsar_2014 2014 Canada E138 Walleye Sander vitreus vertebrate freshwater fish 14.450000 PFTA 14 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C016 NA 5 5 ng/g 0.0690000 NA 0.0090000 sd biological 5 5 0.0870000 NA 0.0340000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0090000 0.0340000
F003 Bhavsar_2014 2014 Canada E139 Walleye Sander vitreus vertebrate freshwater fish 14.450000 PFHxS 6 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11 C016 NA 5 5 ng/g 0.0800000 NA 0.0250000 sd biological 5 5 0.0830000 NA 0.0270000 biological NA ng/g Probably <0.006 Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0250000 0.0270000
F003 Bhavsar_2014 2014 Canada E140 Walleye Sander vitreus vertebrate freshwater fish 14.450000 PFOS 8 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11 C016 NA 5 5 ng/g 36.7900000 NA 1.6240000 sd biological 5 5 44.5100000 NA 7.7180000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 1.6240000 7.7180000
F003 Bhavsar_2014 2014 Canada E141 Walleye Sander vitreus vertebrate freshwater fish 14.450000 PFDS 10 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C016 NA 5 5 ng/g 0.1060000 NA 0.0240000 sd biological 5 5 0.1570000 NA 0.0660000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0240000 0.0660000
F003 Bhavsar_2014 2014 Canada E142 Walleye Sander vitreus vertebrate freshwater fish 14.450000 6:6PFPIA 12 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C016 NA 5 5 ng/g 0.0260000 NA 0.0060000 sd biological 5 5 0.0290000 NA 0.0040000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0060000 0.0040000
F003 Bhavsar_2014 2014 Canada E143 Walleye Sander vitreus vertebrate freshwater fish 14.450000 6:8PFPIA 14 NA No Frying oil-based NA 175 600 No Yes canola oil 11 C016 NA 5 5 ng/g 0.0670000 NA 0.0100000 sd biological 5 5 0.0770000 NA 0.0050000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0100000 0.0050000
F005 DelGobbo_2008 2008 Canada E144 Catfish Ictalurus punctatus vertebrate freshwater fish NA PFOS 8 linear Yes Frying oil-based NA 163 900 No Yes sesame oil 68 C017 NA 19 1 ng/g 1.5657252 NA NA Not available because sample size is one. technical 19 1 0.8987374 NA NA technical 4 ng/g 0.3646058391 1.093817517 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species ML NA NA
F005 DelGobbo_2008 2008 Canada E145 Grouper Epinephelus itajara vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based NA 163 900 No Yes sesame oil 66 C018 NA 14 1 ng/g 1.3600000 NA NA Not available because sample size is one. technical 14 1 0.0169896 LOD NA technical 4 ng/g 0.01698962618 0.05096887855 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F005 DelGobbo_2008 2008 Canada E146 Grouper Epinephelus itajara vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based NA 163 900 No Yes sesame oil 66 C018 NA 14 1 ng/g 0.3715856 LOD NA Not available because sample size is one. technical 14 1 0.4700000 NA NA technical 4 ng/g 0.3715856481 1.114756944 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F005 DelGobbo_2008 2008 Canada E147 Monkfish Lophius americanus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 420 Yes No NA 29520 C019 NA 9 1 ng/g 0.0774969 LOD NA Not available because sample size is one. technical 9 1 0.0600000 NA NA technical 4 ng/g 0.07749693852 0.2324908155 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F005 DelGobbo_2008 2008 Canada E148 Monkfish Lophius americanus vertebrate marine fish NA PFNA 9 NA Yes Boiling water-based NA 100 420 Yes No NA 29520 C019 NA 9 1 ng/g 1.3400000 NA NA Not available because sample size is one. technical 9 1 0.0032120 LOD NA technical 4 ng/g 0.0032120281 0.009636084301 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F005 DelGobbo_2008 2008 Canada E149 Monkfish Lophius americanus vertebrate marine fish NA PFUnDA 11 NA Yes Boiling water-based NA 100 420 Yes No NA 29520 C019 NA 9 1 ng/g 0.0270203 LOD NA Not available because sample size is one. technical 9 1 0.3900000 NA NA technical 4 ng/g 0.02702032357 0.08106097072 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F005 DelGobbo_2008 2008 Canada E150 Monkfish Lophius americanus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 420 Yes No NA 29520 C019 NA 9 1 ng/g 1.3400000 NA NA Not available because sample size is one. technical 9 1 0.2200000 NA NA technical 4 ng/g 0.2333732266 0.7001196799 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F005 DelGobbo_2008 2008 Canada E151 Octopus Bathypolypus arcticus invertebrate mollusca NA PFOA 8 NA Yes Boiling water-based NA 100 420 Yes No NA 57719 C020 NA 15 1 ng/g 0.7800000 NA NA Not available because sample size is one. technical 15 1 0.0600000 NA NA technical 3 ng/g 0.02612585327 0.0783775598 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F005 DelGobbo_2008 2008 Canada E152 Octopus Bathypolypus arcticus invertebrate mollusca NA PFNA 9 NA Yes Boiling water-based NA 100 420 Yes No NA 57719 C020 NA 15 1 ng/g 1.2900000 NA NA Not available because sample size is one. technical 15 1 0.0261259 LOD NA technical 3 ng/g 0.02612585327 0.0783775598 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F005 DelGobbo_2008 2008 Canada E153 Octopus Bathypolypus arcticus invertebrate mollusca NA PFDA 10 NA No Boiling water-based NA 100 420 Yes No NA 57719 C020 NA 15 1 ng/g 1.5500000 NA NA Not available because sample size is one. technical 15 1 0.0120876 LOD NA technical 3 ng/g 0.01208759187 0.03626277562 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F005 DelGobbo_2008 2008 Canada E154 Octopus Bathypolypus arcticus invertebrate mollusca NA PFUnDA 11 NA Yes Boiling water-based NA 100 420 Yes No NA 57719 C020 NA 15 1 ng/g 1.8800000 NA NA Not available because sample size is one. technical 15 1 1.5900000 NA NA technical 3 ng/g 0.02340346342 0.07021039026 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F005 DelGobbo_2008 2008 Canada E155 Octopus Bathypolypus arcticus invertebrate mollusca NA PFTA 14 NA No Boiling water-based NA 100 420 Yes No NA 57719 C020 NA 15 1 ng/g 2.6100000 NA NA Not available because sample size is one. technical 15 1 0.0071943 LOD NA technical 3 ng/g 0.007194278092 0.02158283428 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F005 DelGobbo_2008 2008 Canada E156 Octopus Bathypolypus arcticus invertebrate mollusca NA PFOS 8 linear Yes Boiling water-based NA 100 420 Yes No NA 57719 C020 NA 15 1 ng/g 0.5086163 LOD NA Not available because sample size is one. technical 15 1 0.2300000 NA NA technical 3 ng/g 0.5086163051 1.525848915 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F005 DelGobbo_2008 2008 Canada E157 Red snapper Lutjanus campechanus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 420 Yes No NA 27637 C021 NA 19 1 ng/g 1.4600000 NA NA Not available because sample size is one. technical 19 1 0.2100000 NA NA technical 4 ng/g 0.335745729 1.007237187 Shared control Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F005 DelGobbo_2008 2008 Canada E158 Red snapper Lutjanus campechanus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based NA 163 900 No Yes sesame oil 63 C021 NA 19 1 ng/g 1.4600000 NA NA Not available because sample size is one. technical 19 1 0.7800000 NA NA technical 4 ng/g 0.2127077334 0.6381232001 Shared control Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F005 DelGobbo_2008 2008 Canada E159 Sea squirt Diplosoma listerianum vertebrate tunicata NA PFOA 8 NA Yes Boiling water-based NA 100 420 Yes No NA 29770 C022 NA 22 1 ng/g 1.5800000 NA NA Not available because sample size is one. technical 22 1 1.5900000 NA NA technical 3 ng/g 0.03079926295 0.09239778884 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F005 DelGobbo_2008 2008 Canada E160 Sea squirt Diplosoma listerianum vertebrate tunicata NA PFNA 9 NA Yes Boiling water-based NA 100 420 Yes No NA 29770 C022 NA 22 1 ng/g 1.3200000 NA NA Not available because sample size is one. technical 22 1 0.9600000 NA NA technical 3 ng/g 0.004661629686 0.01398488906 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F005 DelGobbo_2008 2008 Canada E161 Skate Amblyraja hyperborea vertebrate tunicata NA PFNA 9 NA Yes Boiling water-based NA 100 420 Yes No NA 59777 C023 NA 14 1 ng/g 1.0900000 NA NA Not available because sample size is one. technical 14 1 0.0027709 LOD NA technical 4 ng/g 0.002770915071 0.008312745212 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F005 DelGobbo_2008 2008 Canada E162 Skate Amblyraja hyperborea vertebrate tunicata NA PFUnDA 11 NA Yes Boiling water-based NA 100 420 Yes No NA 59777 C023 NA 14 1 ng/g 1.5500000 NA NA Not available because sample size is one. technical 14 1 1.3500000 NA NA technical 4 ng/g 0.01203365344 0.03610096033 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F005 DelGobbo_2008 2008 Canada E163 Skate Amblyraja hyperborea vertebrate tunicata NA PFDoDA 12 NA No Boiling water-based NA 100 420 Yes No NA 59777 C023 NA 14 1 ng/g 1.3300000 NA NA Not available because sample size is one. technical 14 1 0.0255728 LOD NA technical 4 ng/g 0.02557281543 0.07671844628 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F005 DelGobbo_2008 2008 Canada E164 Skate Amblyraja hyperborea vertebrate tunicata NA PFTA 14 NA No Boiling water-based NA 100 420 Yes No NA 59777 C023 NA 14 1 ng/g 0.6700000 NA NA Not available because sample size is one. technical 14 1 0.0070174 LOD NA technical 4 ng/g 0.007017439682 0.02105231905 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F005 DelGobbo_2008 2008 Canada E165 Skate Amblyraja hyperborea vertebrate tunicata NA PFOS 8 linear Yes Boiling water-based NA 100 420 Yes No NA 59777 C023 NA 14 1 ng/g 1.5100000 NA NA Not available because sample size is one. technical 14 1 0.8800000 NA NA technical 4 ng/g 0.3642166626 1.092649988 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F005 DelGobbo_2008 2008 Canada E166 Yellow croaker Larimichthys polyactis vertebrate tunicata NA PFUnDA 11 NA Yes Boiling water-based NA 100 420 Yes No NA 23832 C024 NA 35 1 ng/g 1.5700000 NA NA Not available because sample size is one. technical 35 1 0.0179042 LOD NA technical 4 ng/g 0.0179042065 0.0537126195 Shared control Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F005 DelGobbo_2008 2008 Canada E167 Yellow croaker Larimichthys polyactis vertebrate tunicata NA PFOS 8 linear Yes Boiling water-based NA 100 420 Yes No NA 23832 C024 NA 35 1 ng/g 1.6800000 NA NA Not available because sample size is one. technical 35 1 0.8900000 NA NA technical 4 ng/g 0.3768854178 1.130656253 Shared control Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F005 DelGobbo_2008 2008 Canada E168 Yellow croaker Larimichthys polyactis vertebrate tunicata NA PFUnDA 11 NA Yes Frying oil-based NA 163 900 No Yes sesame oil 50 C025 NA 35 1 ng/g 1.5700000 NA NA Not available because sample size is one. technical 35 1 2.1100000 NA NA technical 4 ng/g 0.0165860278 0.04975808341 Shared control Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F005 DelGobbo_2008 2008 Canada E169 Yellow croaker Larimichthys polyactis vertebrate tunicata NA PFOS 8 linear Yes Frying oil-based NA 163 900 No Yes sesame oil 50 C025 NA 35 1 ng/g 1.6800000 NA NA Not available because sample size is one. technical 35 1 0.6800000 NA NA technical 4 ng/g 0.3921755285 1.176526586 Shared control Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA
F006 Hu_2020 2020 China E170 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 14.380000 PFBA 3 NA No Steaming water-based on a stainless-steel plate in a steamer 100 480 Yes No NA NA C026 NA 5 5 ng/g 6.9619423 NA 7.4193907 sd biological 5 5 5.3412073 NA 1.6889253 biological NA ng/g Not provided 12.2 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. ML 7.4193907 1.6889253
F006 Hu_2020 2020 China E171 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 14.380000 PFOA 8 NA Yes Steaming water-based on a stainless-steel plate in a steamer 100 480 Yes No NA NA C026 NA 5 5 ng/g 0.2098410 NA 0.1560332 sd biological 5 5 0.2674068 NA 0.0800584 biological NA ng/g Not provided 0.226 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 0.1560332 0.0800584
F006 Hu_2020 2020 China E172 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 14.380000 PFBS 4 NA Yes Steaming water-based on a stainless-steel plate in a steamer 100 480 Yes No NA NA C026 NA 5 5 ng/g 24.8753463 NA 23.9889753 sd biological 5 5 23.9801208 NA 26.8453690 biological NA ng/g Not provided 1.01 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 23.9889753 26.8453690
F006 Hu_2020 2020 China E173 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 14.380000 PFOS 8 NA Yes Steaming water-based on a stainless-steel plate in a steamer 100 480 Yes No NA NA C026 NA 5 5 ng/g 86.6890380 NA 39.4592027 sd biological 5 5 122.4133110 NA 62.4690572 biological NA ng/g Not provided 1.57 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 39.4592027 62.4690572
F006 Hu_2020 2020 China E174 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 14.380000 PFHpA 7 NA No Steaming water-based on a stainless-steel plate in a steamer 100 480 Yes No NA NA C026 NA 5 5 ng/g 24.2980562 NA 30.6129835 sd biological 5 5 55.3995680 NA 55.3995680 biological NA ng/g Not provided 0.47 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 30.6129835 55.3995680
F006 Hu_2020 2020 China E175 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 14.380000 PFDoDA 12 NA No Steaming water-based on a stainless-steel plate in a steamer 100 480 Yes No NA NA C026 NA 5 5 ng/g 1.5680310 NA 0.5599538 sd biological 5 5 2.2676991 NA 1.5334164 biological NA ng/g Not provided 0.093 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 0.5599538 1.5334164
F006 Hu_2020 2020 China E176 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 14.380000 PFHxS 6 NA Yes Steaming water-based on a stainless-steel plate in a steamer 100 480 Yes No NA NA C026 NA 5 5 ng/g 1.8092949 NA 2.3827419 sd biological 5 5 0.8685897 NA 0.3034431 biological NA ng/g Not provided 0.155 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 2.3827419 0.3034431
F006 Hu_2020 2020 China E177 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 14.380000 FOSA 8 NA Yes Steaming water-based on a stainless-steel plate in a steamer 100 480 Yes No NA NA C026 NA 5 5 ng/g 2.5990437 NA 1.6889253 sd biological 5 5 2.3838798 NA 1.2904183 biological NA ng/g Not provided 0.026 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 1.6889253 1.2904183
F006 Hu_2020 2020 China E178 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 16.480000 PFBA 3 NA No Boiling water-based NA 100 120 Yes No NA 300 C027 NA 5 5 ng/g 6.9619423 NA 7.4193907 sd biological 5 5 4.9146982 NA 7.4344664 biological NA ng/g Not provided 12.2 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 7.4193907 7.4344664
F006 Hu_2020 2020 China E179 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 16.480000 PFOA 8 NA Yes Boiling water-based NA 100 120 Yes No NA 300 C027 NA 5 5 ng/g 0.2098410 NA 0.1560332 sd biological 5 5 0.1932566 NA 0.0707998 biological NA ng/g Not provided 0.226 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 0.1560332 0.0707998
F006 Hu_2020 2020 China E180 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 16.480000 PFBS 4 NA Yes Boiling water-based NA 100 120 Yes No NA 300 C027 NA 5 5 ng/g 24.8753463 NA 23.9889753 sd biological 5 5 10.8230680 NA 7.4606797 biological NA ng/g Not provided 1.01 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 23.9889753 7.4606797
F006 Hu_2020 2020 China E181 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 16.480000 PFOS 8 NA Yes Boiling water-based NA 100 120 Yes No NA 300 C027 NA 5 5 ng/g 86.6890380 NA 39.4592027 sd biological 5 5 97.7348993 NA 23.1725546 biological NA ng/g Not provided 1.57 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 39.4592027 23.1725546
F006 Hu_2020 2020 China E182 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 16.480000 PFHpA 7 NA No Boiling water-based NA 100 120 Yes No NA 300 C027 NA 5 5 ng/g 24.2980562 NA 30.6129835 sd biological 5 5 13.7149028 NA 23.6036055 biological NA ng/g Not provided 0.47 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 30.6129835 23.6036055
F006 Hu_2020 2020 China E183 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 16.480000 PFDoDA 12 NA No Boiling water-based NA 100 120 Yes No NA 300 C027 NA 5 5 ng/g 1.5680310 NA 0.5599538 sd biological 5 5 2.3534292 NA 2.4839931 biological NA ng/g Not provided 0.093 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 0.5599538 2.4839931
F006 Hu_2020 2020 China E184 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 16.480000 PFHxS 6 NA Yes Boiling water-based NA 100 120 Yes No NA 300 C027 NA 5 5 ng/g 1.8092949 NA 2.3827419 sd biological 5 5 0.6506410 NA 0.1079317 biological NA ng/g Not provided 0.155 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 2.3827419 0.1079317
F006 Hu_2020 2020 China E185 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 16.480000 FOSA 8 NA Yes Boiling water-based NA 100 120 Yes No NA 300 C027 NA 5 5 ng/g 2.5990437 NA 1.6889253 sd biological 5 5 2.2540984 NA 1.2484167 biological NA ng/g Not provided 0.026 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 1.6889253 1.2484167
F006 Hu_2020 2020 China E186 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 23.140000 PFBA 3 NA No Frying oil-based In a stainless-steel pan 180 150 No Yes Not specified 100 C028 NA 5 5 ng/g 6.9619423 NA 7.4193907 sd biological 5 5 7.9068241 NA 9.3812679 biological NA ng/g Not provided 12.2 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 7.4193907 9.3812679
F006 Hu_2020 2020 China E187 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 23.140000 PFOA 8 NA Yes Frying oil-based In a stainless-steel pan 180 150 No Yes Not specified 100 C028 NA 5 5 ng/g 0.2098410 NA 0.1560332 sd biological 5 5 0.2308114 NA 0.1541468 biological NA ng/g Not provided 0.226 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 0.1560332 0.1541468
F006 Hu_2020 2020 China E188 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 23.140000 PFBS 4 NA Yes Frying oil-based In a stainless-steel pan 180 150 No Yes Not specified 100 C028 NA 5 5 ng/g 24.8753463 NA 23.9889753 sd biological 5 5 9.8657220 NA 5.8014926 biological NA ng/g Not provided 1.01 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 23.9889753 5.8014926
F006 Hu_2020 2020 China E189 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 23.140000 PFOS 8 NA Yes Frying oil-based In a stainless-steel pan 180 150 No Yes Not specified 100 C028 NA 5 5 ng/g 86.6890380 NA 39.4592027 sd biological 5 5 134.4379195 NA 58.0538019 biological NA ng/g Not provided 1.57 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 39.4592027 58.0538019
F006 Hu_2020 2020 China E190 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 23.140000 PFHpA 7 NA No Frying oil-based In a stainless-steel pan 180 150 No Yes Not specified 100 C028 NA 5 5 ng/g 24.2980562 NA 30.6129835 sd biological 5 5 23.7041037 NA 35.9297367 biological NA ng/g Not provided 0.47 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 30.6129835 35.9297367
F006 Hu_2020 2020 China E191 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 23.140000 PFDoDA 12 NA No Frying oil-based In a stainless-steel pan 180 150 No Yes Not specified 100 C028 NA 5 5 ng/g 1.5680310 NA 0.5599538 sd biological 5 5 2.8733407 NA 2.7470061 biological NA ng/g Not provided 0.093 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 0.5599538 2.7470061
F006 Hu_2020 2020 China E192 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 23.140000 PFHxS 6 NA Yes Frying oil-based In a stainless-steel pan 180 150 No Yes Not specified 100 C028 NA 5 5 ng/g 1.8092949 NA 2.3827419 sd biological 5 5 1.1602564 NA 0.7375647 biological NA ng/g Not provided 0.155 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 2.3827419 0.7375647
F006 Hu_2020 2020 China E193 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 23.140000 FOSA 8 NA Yes Frying oil-based In a stainless-steel pan 180 150 No Yes Not specified 100 C028 NA 5 5 ng/g 2.5990437 NA 1.6889253 sd biological 5 5 3.7500000 NA 3.7411362 biological NA ng/g Not provided 0.026 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 1.6889253 3.7411362
F006 Hu_2020 2020 China E194 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 21.310000 PFBA 3 NA No Grilling oil-based Domestic electric oven set to broil 210 600 No Yes Not specified 10 C029 NA 5 5 ng/g 6.9619423 NA 7.4193907 sd biological 5 5 4.8490814 NA 6.9303363 biological NA ng/g Not provided 12.2 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 7.4193907 6.9303363
F006 Hu_2020 2020 China E195 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 21.310000 PFOA 8 NA Yes Grilling oil-based Domestic electric oven set to broil 210 600 No Yes Not specified 10 C029 NA 5 5 ng/g 0.2098410 NA 0.1560332 sd biological 5 5 0.1652961 NA 0.0630496 biological NA ng/g Not provided 0.226 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 0.1560332 0.0630496
F006 Hu_2020 2020 China E196 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 21.310000 PFBS 4 NA Yes Grilling oil-based Domestic electric oven set to broil 210 600 No Yes Not specified 10 C029 NA 5 5 ng/g 24.8753463 NA 23.9889753 sd biological 5 5 7.5376305 NA 1.5022632 biological NA ng/g Not provided 1.01 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 23.9889753 1.5022632
F006 Hu_2020 2020 China E197 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 21.310000 PFOS 8 NA Yes Grilling oil-based Domestic electric oven set to broil 210 600 No Yes Not specified 10 C029 NA 5 5 ng/g 86.6890380 NA 39.4592027 sd biological 5 5 121.7142058 NA 62.5574247 biological NA ng/g Not provided 1.57 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 39.4592027 62.5574247
F006 Hu_2020 2020 China E198 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 21.310000 PFHpA 7 NA No Grilling oil-based Domestic electric oven set to broil 210 600 No Yes Not specified 10 C029 NA 5 5 ng/g 24.2980562 NA 30.6129835 sd biological 5 5 10.0971922 NA 16.4902451 biological NA ng/g Not provided 0.47 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 30.6129835 16.4902451
F006 Hu_2020 2020 China E199 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 21.310000 PFDoDA 12 NA No Grilling oil-based Domestic electric oven set to broil 210 600 No Yes Not specified 10 C029 NA 5 5 ng/g 1.5680310 NA 0.5599538 sd biological 5 5 2.9120575 NA 3.3602781 biological NA ng/g Not provided 0.093 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 0.5599538 3.3602781
F006 Hu_2020 2020 China E200 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 21.310000 PFHxS 6 NA Yes Grilling oil-based Domestic electric oven set to broil 210 600 No Yes Not specified 10 C029 NA 5 5 ng/g 1.8092949 NA 2.3827419 sd biological 5 5 0.8253205 NA 0.2542197 biological NA ng/g Not provided 0.155 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 2.3827419 0.2542197
F006 Hu_2020 2020 China E201 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 21.310000 FOSA 8 NA Yes Grilling oil-based Domestic electric oven set to broil 210 600 No Yes Not specified 10 C029 NA 5 5 ng/g 2.5990437 NA 1.6889253 sd biological 5 5 2.2814208 NA 0.4304018 biological NA ng/g Not provided 0.026 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 1.6889253 0.4304018
F007 Kim_2020 2020 Korea E202 Mackerel Scomber japonicus vertebrate marine fish NA PFOA 8 NA Yes Grilling oil-based NA NA 360 No Yes Not specified 5 C030 NA 10 1 ng/g 0.0700000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. ML NA NA
F007 Kim_2020 2020 Korea E203 Mackerel Scomber japonicus vertebrate marine fish NA PFOA 8 NA Yes Braising water-based NA 100 1500 Yes No NA 250 C031 NA 10 1 ng/g 0.0700000 NA 0.0100000 sd technical 10 1 0.1100000 NA 0.0000000 technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. NA NA NA
F007 Kim_2020 2020 Korea E204 Mackerel Scomber japonicus vertebrate marine fish NA PFOA 8 NA Yes Steaming water-based NA 100 900 Yes No NA 250 C032 NA 10 1 ng/g 0.0700000 NA 0.0100000 sd technical 10 1 0.0200000 NA NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. NA NA NA
F007 Kim_2020 2020 Korea E205 Mackerel Scomber japonicus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based NA 160 300 No Yes Not specified 750 C033 NA 10 1 ng/g 0.0700000 NA 0.0100000 sd technical 10 1 0.0900000 NA 0.0600000 technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. NA NA NA
F007 Kim_2020 2020 Korea E206 Mackerel Scomber japonicus vertebrate marine fish NA PFBA 3 NA No Grilling oil-based NA NA 360 No Yes Not specified 5 C030 NA 10 1 ng/g 0.1600000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. NA NA NA
F007 Kim_2020 2020 Korea E207 Mackerel Scomber japonicus vertebrate marine fish NA PFBA 3 NA No Braising water-based NA 100 1500 Yes No NA 250 C031 NA 10 1 ng/g 0.1600000 NA 0.0100000 sd technical 10 1 0.1300000 NA 0.0400000 technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. NA NA NA
F007 Kim_2020 2020 Korea E208 Mackerel Scomber japonicus vertebrate marine fish NA PFBA 3 NA No Steaming water-based NA 100 900 Yes No NA 250 C032 NA 10 1 ng/g 0.1600000 NA 0.0100000 sd technical 10 1 0.1400000 NA 0.0100000 technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. NA NA NA
F007 Kim_2020 2020 Korea E209 Mackerel Scomber japonicus vertebrate marine fish NA PFBA 3 NA No Frying oil-based NA 160 300 No Yes Not specified 750 C033 NA 10 1 ng/g 0.1600000 NA 0.0100000 sd technical 10 1 0.0900000 NA 0.0000000 technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. NA NA NA
F007 Kim_2020 2020 Korea E210 Mackerel Scomber japonicus vertebrate marine fish NA PFHpA 7 NA No Grilling oil-based NA NA 360 No Yes Not specified 5 C030 NA 10 1 ng/g 0.0700000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. NA NA NA
F007 Kim_2020 2020 Korea E211 Mackerel Scomber japonicus vertebrate marine fish NA PFHpA 7 NA No Braising water-based NA 100 1500 Yes No NA 250 C031 NA 10 1 ng/g 0.0700000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. NA NA NA
F007 Kim_2020 2020 Korea E212 Mackerel Scomber japonicus vertebrate marine fish NA PFHpA 7 NA No Steaming water-based NA 100 900 Yes No NA 250 C032 NA 10 1 ng/g 0.0700000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. NA NA NA
F007 Kim_2020 2020 Korea E213 Mackerel Scomber japonicus vertebrate marine fish NA PFHpA 7 NA No Frying oil-based NA 160 300 No Yes Not specified 750 C033 NA 10 1 ng/g 0.0700000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. NA NA NA
F007 Kim_2020 2020 Korea E214 Mackerel Scomber japonicus vertebrate marine fish NA PFDoDA 12 NA No Grilling oil-based NA NA 360 No Yes Not specified 5 C030 NA 10 1 ng/g 0.0200000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. NA NA NA
F007 Kim_2020 2020 Korea E215 Mackerel Scomber japonicus vertebrate marine fish NA PFDoDA 12 NA No Braising water-based NA 100 1500 Yes No NA 250 C031 NA 10 1 ng/g 0.0200000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. NA NA NA
F007 Kim_2020 2020 Korea E216 Mackerel Scomber japonicus vertebrate marine fish NA PFDoDA 12 NA No Steaming water-based NA 100 900 Yes No NA 250 C032 NA 10 1 ng/g 0.0200000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. NA NA NA
F007 Kim_2020 2020 Korea E217 Mackerel Scomber japonicus vertebrate marine fish NA PFDoDA 12 NA No Frying oil-based NA 160 300 No Yes Not specified 750 C033 NA 10 1 ng/g 0.0200000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. NA NA NA
F007 Kim_2020 2020 Korea E218 Mackerel Scomber japonicus vertebrate marine fish NA PFTrA 13 NA No Grilling oil-based NA NA 360 No Yes Not specified 5 C030 NA 10 1 ng/g 0.0800000 NA 0.0100000 sd technical 10 1 0.0500000 NA 0.0000000 technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. NA NA NA
F007 Kim_2020 2020 Korea E219 Mackerel Scomber japonicus vertebrate marine fish NA PFTrA 13 NA No Braising water-based NA 100 1500 Yes No NA 250 C031 NA 10 1 ng/g 0.0800000 NA 0.0100000 sd technical 10 1 0.0600000 NA 0.0200000 technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. NA NA NA
F007 Kim_2020 2020 Korea E220 Mackerel Scomber japonicus vertebrate marine fish NA PFTrA 13 NA No Steaming water-based NA 100 900 Yes No NA 250 C032 NA 10 1 ng/g 0.0800000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. NA NA NA
F007 Kim_2020 2020 Korea E221 Mackerel Scomber japonicus vertebrate marine fish NA PFTrA 13 NA No Frying oil-based NA 160 300 No Yes Not specified 750 C033 NA 10 1 ng/g 0.0800000 NA 0.0100000 sd technical 10 1 0.0600000 NA 0.0000000 technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. NA NA NA
F007 Kim_2020 2020 Korea E222 Mackerel Scomber japonicus vertebrate marine fish NA PFBS 4 NA Yes Grilling oil-based NA NA 360 No Yes Not specified 5 C030 NA 10 1 ng/g 0.1900000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. NA NA NA
F007 Kim_2020 2020 Korea E223 Mackerel Scomber japonicus vertebrate marine fish NA PFBS 4 NA Yes Braising water-based NA 100 1500 Yes No NA 250 C031 NA 10 1 ng/g 0.1900000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. NA NA NA
F007 Kim_2020 2020 Korea E224 Mackerel Scomber japonicus vertebrate marine fish NA PFBS 4 NA Yes Steaming water-based NA 100 900 Yes No NA 250 C032 NA 10 1 ng/g 0.1900000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. NA NA NA
F007 Kim_2020 2020 Korea E225 Mackerel Scomber japonicus vertebrate marine fish NA PFBS 4 NA Yes Frying oil-based NA 160 300 No Yes Not specified 750 C033 NA 10 1 ng/g 0.1900000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. NA NA NA
F008 Luo_2019 2019 Korea E316 Swimming crab Portunus trituberculatus invertebrate crustacea NA PFOA 8 NA Yes Boiling water-based Boiled with radish 100 600 Yes No NA 2500 C040 NA 5 1 ng/g 20.7900000 NA 0.1700000 sd technical 5 1 16.7700000 NA 0.4200000 technical NA ng/g 0.06 0.19 Dependent Table 4 No Scientific name of swimming crab not provided in paper, inferred as this species of swimming crab is commonly eaten in South korea (Kim, S., Lee, M.J., Lee, J.J., Choi, S.H. and Kim, B.S., 2017. Analysis of microbiota of the swimming crab (Portunus trituberculatus) in South Korea to identify risk markers for foodborne illness. LWT, 86, pp.483-491.) NA NA NA
F008 Luo_2019 2019 Korea E317 Swimming crab Portunus trituberculatus invertebrate crustacea NA PFOS 8 NA Yes Boiling water-based Boiled with radish 100 600 Yes No NA 2500 C040 NA 5 1 ng/g 0.8100000 NA 0.0200000 sd technical 5 1 0.7400000 NA 0.0300000 technical NA ng/g 0.07 0.07 Dependent Table 4 No NA NA NA NA
F008 Luo_2019 2019 Korea E318 Swimming crab Portunus trituberculatus invertebrate crustacea NA PFBA 3 NA No Boiling water-based Boiled with radish 100 600 Yes No NA 2500 C040 NA 5 1 ng/g 0.1400000 NA 0.0100000 sd technical 5 1 0.0400000 NA 0.0100000 technical NA ng/g 0.06 0.19 Dependent Table 4 No NA NA NA NA
F008 Luo_2019 2019 Korea E319 Swimming crab Portunus trituberculatus invertebrate crustacea NA PFHpA 7 NA No Boiling water-based Boiled with radish 100 600 Yes No NA 2500 C040 NA 5 1 ng/g 0.3700000 NA 0.0300000 sd technical 5 1 0.3200000 NA 0.0100000 technical NA ng/g 0.06 0.17 Dependent Table 4 No NA NA NA NA
F008 Luo_2019 2019 Korea E320 Swimming crab Portunus trituberculatus invertebrate crustacea NA PFNA 9 NA Yes Boiling water-based Boiled with radish 100 600 Yes No NA 2500 C040 NA 5 1 ng/g 2.8900000 NA 0.0200000 sd technical 5 1 2.3000000 NA 0.0300000 technical NA ng/g 0.03 0.08 Dependent Table 4 No NA NA NA NA
F008 Luo_2019 2019 Korea E321 Swimming crab Portunus trituberculatus invertebrate crustacea NA PFDA 10 NA Yes Boiling water-based Boiled with radish 100 600 Yes No NA 2500 C040 NA 5 1 ng/g 0.6600000 NA 0.0200000 sd technical 5 1 0.5700000 NA 0.0200000 technical NA ng/g 0.04 0.11 Dependent Table 4 No NA NA NA NA
F008 Luo_2019 2019 Korea E322 Swimming crab Portunus trituberculatus invertebrate crustacea NA PFUnDA 11 NA Yes Boiling water-based Boiled with radish 100 600 Yes No NA 2500 C040 NA 5 1 ng/g 0.9300000 NA 0.0100000 sd technical 5 1 0.7900000 NA 0.0200000 technical NA ng/g 0.08 0.25 Dependent Table 4 No NA NA NA NA
F008 Luo_2019 2019 Korea E323 Swimming crab Portunus trituberculatus invertebrate crustacea NA PFDoDA 12 NA No Boiling water-based Boiled with radish 100 600 Yes No NA 2500 C040 NA 5 1 ng/g 0.2500000 NA 0.0200000 sd technical 5 1 0.2300000 NA 0.0100000 technical NA ng/g 0.06 0.19 Dependent Table 4 No NA NA NA NA
F008 Luo_2019 2019 Korea E324 Swimming crab Portunus trituberculatus invertebrate crustacea NA PFTrA 13 NA No Boiling water-based Boiled with radish 100 600 Yes No NA 2500 C040 NA 5 1 ng/g 1.1200000 NA 0.0600000 sd technical 5 1 1.3800000 NA 0.0900000 technical NA ng/g 0.05 0.16 Dependent Table 4 No NA NA NA NA
F008 Luo_2019 2019 Korea E325 Swimming crab Portunus trituberculatus invertebrate crustacea NA PFTA 14 NA No Boiling water-based Boiled with radish 100 600 Yes No NA 2500 C040 NA 5 1 ng/g 0.2800000 NA 0.0100000 sd technical 5 1 0.2600000 NA 0.0200000 technical NA ng/g 0.05 0.15 Dependent Table 4 No NA NA NA NA
F008 Luo_2019 2019 Korea E326 Swimming crab Portunus trituberculatus invertebrate crustacea NA PFHxS 6 NA Yes Boiling water-based Boiled with radish 100 600 Yes No NA 2500 C040 NA 5 1 ng/g 0.4800000 NA 0.0300000 sd technical 5 1 0.3300000 NA 0.0300000 technical NA ng/g 0.08 0.25 Dependent Table 4 No NA NA NA NA
F008 Luo_2019 2019 Korea E327 Swimming crab Portunus trituberculatus invertebrate crustacea NA PFDS 10 NA No Boiling water-based Boiled with radish 100 600 Yes No NA 2500 C040 NA 5 1 ng/g 0.0400000 NA 0.0100000 sd technical 5 1 0.0400000 NA 0.0100000 technical NA ng/g 0.09 0.27 Dependent Table 4 No NA NA NA NA
F008 Luo_2019 2019 Korea E328 Swimming crab Portunus trituberculatus invertebrate crustacea NA FOSA 8 NA Yes Boiling water-based Boiled with radish 100 600 Yes No NA 2500 C040 NA 5 1 ng/g 1.5400000 NA 0.0900000 sd technical 5 1 2.5500000 NA 0.1900000 technical NA ng/g 0.04 0.11 Dependent Table 4 No NA NA NA NA
F010 Sungur_2019 2019 Turkey E329 Bluefish Pomatomus saltator vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 600 Yes No NA 300 C041 NA 10 1 ng/g 0.2320000 NA 0.0010000 sd technical 10 1 0.1590000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied ML - note shared controls for differend cooking times and methods NA NA
F010 Sungur_2019 2019 Turkey E330 Bluefish Pomatomus saltator vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 900 Yes No NA 300 C042 NA 10 1 ng/g 0.2320000 NA 0.0010000 sd technical 10 1 0.1170000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E331 Bluefish Pomatomus saltator vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 1200 Yes No NA 300 C043 NA 10 1 ng/g 0.2320000 NA 0.0010000 sd technical 10 1 0.0790000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E332 Bluefish Pomatomus saltator vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 600 No No NA 300 C044 NA 10 1 ng/g 0.2320000 NA 0.0010000 sd technical 10 1 0.1420000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E333 Bluefish Pomatomus saltator vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 900 No No NA 300 C045 NA 10 1 ng/g 0.2320000 NA 0.0010000 sd technical 10 1 0.1160000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E334 Bluefish Pomatomus saltator vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 1200 No No NA 300 C046 NA 10 1 ng/g 0.2320000 NA 0.0010000 sd technical 10 1 0.0980000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E335 Bluefish Pomatomus saltator vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300 C047 NA 10 1 ng/g 0.2320000 NA 0.0010000 sd technical 10 1 0.1400000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E336 Bluefish Pomatomus saltator vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300 C048 NA 10 1 ng/g 0.2320000 NA 0.0010000 sd technical 10 1 0.1330000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E337 Bluefish Pomatomus saltator vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300 C049 NA 10 1 ng/g 0.2320000 NA 0.0010000 sd technical 10 1 0.0710000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E338 Bluefish Pomatomus saltator vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300 C050 NA 10 1 ng/g 0.2320000 NA 0.0010000 sd technical 10 1 0.2010000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E339 Bluefish Pomatomus saltator vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300 C051 NA 10 1 ng/g 0.2320000 NA 0.0010000 sd technical 10 1 0.0590000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E340 Bluefish Pomatomus saltator vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300 C052 NA 10 1 ng/g 0.2320000 NA 0.0010000 sd technical 10 1 0.0480000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E341 Bluefish Pomatomus saltator vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 600 Yes No NA 300 C041 NA 10 1 ng/g 24.0000000 NA 0.0110000 sd technical 10 1 14.7000000 NA 0.0090000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E342 Bluefish Pomatomus saltator vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 900 Yes No NA 300 C042 NA 10 1 ng/g 24.0000000 NA 0.0110000 sd technical 10 1 9.3500000 NA 0.0080000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E343 Bluefish Pomatomus saltator vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 1200 Yes No NA 300 C043 NA 10 1 ng/g 24.0000000 NA 0.0110000 sd technical 10 1 3.6600000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E344 Bluefish Pomatomus saltator vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 600 No No NA 300 C044 NA 10 1 ng/g 24.0000000 NA 0.0110000 sd technical 10 1 5.6300000 NA 0.0050000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E345 Bluefish Pomatomus saltator vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 900 No No NA 300 C045 NA 10 1 ng/g 24.0000000 NA 0.0110000 sd technical 10 1 4.5000000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E346 Bluefish Pomatomus saltator vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 1200 No No NA 300 C046 NA 10 1 ng/g 24.0000000 NA 0.0110000 sd technical 10 1 3.7700000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E347 Bluefish Pomatomus saltator vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300 C047 NA 10 1 ng/g 24.0000000 NA 0.0110000 sd technical 10 1 8.2800000 NA 0.0070000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E348 Bluefish Pomatomus saltator vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300 C048 NA 10 1 ng/g 24.0000000 NA 0.0110000 sd technical 10 1 6.6200000 NA 0.0060000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E349 Bluefish Pomatomus saltator vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300 C049 NA 10 1 ng/g 24.0000000 NA 0.0110000 sd technical 10 1 3.4800000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E350 Bluefish Pomatomus saltator vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300 C050 NA 10 1 ng/g 24.0000000 NA 0.0110000 sd technical 10 1 4.4900000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E351 Bluefish Pomatomus saltator vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300 C051 NA 10 1 ng/g 24.0000000 NA 0.0110000 sd technical 10 1 3.0500000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E352 Bluefish Pomatomus saltator vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300 C052 NA 10 1 ng/g 24.0000000 NA 0.0110000 sd technical 10 1 2.8300000 NA 0.0030000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E353 Red mullet Mullus barbatus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 600 Yes No NA 300 C053 NA 10 1 ng/g 0.2140000 NA 0.0010000 sd technical 10 1 0.1960000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E354 Red mullet Mullus barbatus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 900 Yes No NA 300 C054 NA 10 1 ng/g 0.2140000 NA 0.0010000 sd technical 10 1 0.1180000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E355 Red mullet Mullus barbatus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 1200 Yes No NA 300 C055 NA 10 1 ng/g 0.2140000 NA 0.0010000 sd technical 10 1 0.0840000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E356 Red mullet Mullus barbatus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 600 No No NA NA C056 NA 10 1 ng/g 0.2140000 NA 0.0010000 sd technical 10 1 0.2030000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E357 Red mullet Mullus barbatus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 900 No No NA NA C057 NA 10 1 ng/g 0.2140000 NA 0.0010000 sd technical 10 1 0.1390000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E358 Red mullet Mullus barbatus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 1200 No No NA NA C058 NA 10 1 ng/g 0.2140000 NA 0.0010000 sd technical 10 1 0.1040000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E359 Red mullet Mullus barbatus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300 C059 NA 10 1 ng/g 0.2140000 NA 0.0010000 sd technical 10 1 0.2070000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E360 Red mullet Mullus barbatus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300 C060 NA 10 1 ng/g 0.2140000 NA 0.0010000 sd technical 10 1 0.0970000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E361 Red mullet Mullus barbatus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300 C061 NA 10 1 ng/g 0.2140000 NA 0.0010000 sd technical 10 1 0.0820000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E362 Red mullet Mullus barbatus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300 C062 NA 10 1 ng/g 0.2140000 NA 0.0010000 sd technical 10 1 0.1960000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E363 Red mullet Mullus barbatus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300 C063 NA 10 1 ng/g 0.2140000 NA 0.0010000 sd technical 10 1 0.0510000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E364 Red mullet Mullus barbatus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300 C064 NA 10 1 ng/g 0.2140000 NA 0.0010000 sd technical 10 1 0.2550000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E365 Red mullet Mullus barbatus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 600 Yes No NA 300 C053 NA 10 1 ng/g 8.9200000 NA 0.0070000 sd technical 10 1 4.7800000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E366 Red mullet Mullus barbatus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 900 Yes No NA 300 C054 NA 10 1 ng/g 8.9200000 NA 0.0070000 sd technical 10 1 3.5000000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E367 Red mullet Mullus barbatus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 1200 Yes No NA 300 C055 NA 10 1 ng/g 8.9200000 NA 0.0070000 sd technical 10 1 1.5100000 NA 0.0020000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E368 Red mullet Mullus barbatus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 600 No No NA 300 C056 NA 10 1 ng/g 8.9200000 NA 0.0070000 sd technical 10 1 7.0500000 NA 0.0060000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E369 Red mullet Mullus barbatus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 900 No No NA 300 C057 NA 10 1 ng/g 8.9200000 NA 0.0070000 sd technical 10 1 2.4700000 NA 0.0030000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E370 Red mullet Mullus barbatus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 1200 No No NA 300 C058 NA 10 1 ng/g 8.9200000 NA 0.0070000 sd technical 10 1 1.7600000 NA 0.0020000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E371 Red mullet Mullus barbatus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300 C059 NA 10 1 ng/g 8.9200000 NA 0.0070000 sd technical 10 1 3.0300000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E372 Red mullet Mullus barbatus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300 C060 NA 10 1 ng/g 8.9200000 NA 0.0070000 sd technical 10 1 2.0400000 NA 0.0030000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E373 Red mullet Mullus barbatus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300 C061 NA 10 1 ng/g 8.9200000 NA 0.0070000 sd technical 10 1 1.2300000 NA 0.0020000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E374 Red mullet Mullus barbatus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300 C062 NA 10 1 ng/g 8.9200000 NA 0.0070000 sd technical 10 1 4.2800000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E375 Red mullet Mullus barbatus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300 C063 NA 10 1 ng/g 8.9200000 NA 0.0070000 sd technical 10 1 2.7800000 NA 0.0030000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E376 Red mullet Mullus barbatus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300 C064 NA 10 1 ng/g 8.9200000 NA 0.0070000 sd technical 10 1 1.0200000 NA 0.0020000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E377 Whitefish Salmo trutta vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 600 Yes No NA 300 C065 NA 10 1 ng/g 0.2550000 NA 0.0010000 sd technical 10 1 0.2420000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E378 Whitefish Salmo trutta vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 900 Yes No NA 300 C066 NA 10 1 ng/g 0.2550000 NA 0.0010000 sd technical 10 1 0.1870000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E379 Whitefish Salmo trutta vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 1200 Yes No NA 300 C067 NA 10 1 ng/g 0.2550000 NA 0.0010000 sd technical 10 1 0.0960000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E380 Whitefish Salmo trutta vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 600 No No NA 300 C068 NA 10 1 ng/g 0.2550000 NA 0.0010000 sd technical 10 1 0.1750000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E381 Whitefish Salmo trutta vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 900 No No NA 300 C069 NA 10 1 ng/g 0.2550000 NA 0.0010000 sd technical 10 1 0.1530000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E382 Whitefish Salmo trutta vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 1200 No No NA 300 C070 NA 10 1 ng/g 0.2550000 NA 0.0010000 sd technical 10 1 0.0980000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E383 Whitefish Salmo trutta vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300 C071 NA 10 1 ng/g 0.2550000 NA 0.0010000 sd technical 10 1 0.1890000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E384 Whitefish Salmo trutta vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300 C072 NA 10 1 ng/g 0.2550000 NA 0.0010000 sd technical 10 1 0.1320000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E385 Whitefish Salmo trutta vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300 C073 NA 10 1 ng/g 0.2550000 NA 0.0010000 sd technical 10 1 0.0930000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E386 Whitefish Salmo trutta vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300 C074 NA 10 1 ng/g 0.2550000 NA 0.0010000 sd technical 10 1 0.1810000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E387 Whitefish Salmo trutta vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300 C075 NA 10 1 ng/g 0.2550000 NA 0.0010000 sd technical 10 1 0.0880000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E388 Whitefish Salmo trutta vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300 C076 NA 10 1 ng/g 0.2550000 NA 0.0010000 sd technical 10 1 0.0660000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E389 Whitefish Salmo trutta vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 600 Yes No NA 300 C065 NA 10 1 ng/g 5.0700000 NA 0.0040000 sd technical 10 1 4.1500000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E390 Whitefish Salmo trutta vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 900 Yes No NA 300 C066 NA 10 1 ng/g 5.0700000 NA 0.0040000 sd technical 10 1 2.6500000 NA 0.0030000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E391 Whitefish Salmo trutta vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 1200 Yes No NA 300 C067 NA 10 1 ng/g 5.0700000 NA 0.0040000 sd technical 10 1 1.2300000 NA 0.0020000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E392 Whitefish Salmo trutta vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 600 No No NA 300 C068 NA 10 1 ng/g 5.0700000 NA 0.0040000 sd technical 10 1 4.4400000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E393 Whitefish Salmo trutta vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 900 No No NA 300 C069 NA 10 1 ng/g 5.0700000 NA 0.0040000 sd technical 10 1 2.3600000 NA 0.0030000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E394 Whitefish Salmo trutta vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 1200 No No NA 300 C070 NA 10 1 ng/g 5.0700000 NA 0.0040000 sd technical 10 1 1.6500000 NA 0.0020000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E395 Whitefish Salmo trutta vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300 C071 NA 10 1 ng/g 5.0700000 NA 0.0040000 sd technical 10 1 3.6800000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E396 Whitefish Salmo trutta vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300 C072 NA 10 1 ng/g 5.0700000 NA 0.0040000 sd technical 10 1 1.7300000 NA 0.0020000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E397 Whitefish Salmo trutta vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300 C073 NA 10 1 ng/g 5.0700000 NA 0.0040000 sd technical 10 1 0.9200000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E398 Whitefish Salmo trutta vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300 C074 NA 10 1 ng/g 5.0700000 NA 0.0040000 sd technical 10 1 4.0300000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E399 Whitefish Salmo trutta vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300 C075 NA 10 1 ng/g 5.0700000 NA 0.0040000 sd technical 10 1 1.9700000 NA 0.0020000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E400 Whitefish Salmo trutta vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300 C076 NA 10 1 ng/g 5.0700000 NA 0.0040000 sd technical 10 1 0.8400000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E401 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 600 Yes No NA 300 C077 NA 10 1 ng/g 0.2380000 NA 0.0010000 sd technical 10 1 0.2020000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E402 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 900 Yes No NA 300 C078 NA 10 1 ng/g 0.2380000 NA 0.0010000 sd technical 10 1 0.1280000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E403 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 1200 Yes No NA 300 C079 NA 10 1 ng/g 0.2380000 NA 0.0010000 sd technical 10 1 0.0920000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E404 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 600 No No NA NA C080 NA 10 1 ng/g 0.2380000 NA 0.0010000 sd technical 10 1 0.1580000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E405 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 900 No No NA NA C081 NA 10 1 ng/g 0.2380000 NA 0.0010000 sd technical 10 1 0.1210000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E406 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 1200 No No NA NA C082 NA 10 1 ng/g 0.2380000 NA 0.0010000 sd technical 10 1 0.0980000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E407 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300 C083 NA 10 1 ng/g 0.2380000 NA 0.0010000 sd technical 10 1 0.1680000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E408 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300 C084 NA 10 1 ng/g 0.2380000 NA 0.0010000 sd technical 10 1 0.1340000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E409 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300 C085 NA 10 1 ng/g 0.2380000 NA 0.0010000 sd technical 10 1 0.0910000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E410 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300 C086 NA 10 1 ng/g 0.2380000 NA 0.0010000 sd technical 10 1 0.1740000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E411 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300 C087 NA 10 1 ng/g 0.2380000 NA 0.0010000 sd technical 10 1 0.0960000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E412 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300 C088 NA 10 1 ng/g 0.2380000 NA 0.0010000 sd technical 10 1 0.0440000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E413 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 600 Yes No NA 300 C077 NA 10 1 ng/g 0.4070000 NA 0.0010000 sd technical 10 1 0.2760000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E414 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 900 Yes No NA 300 C078 NA 10 1 ng/g 0.4070000 NA 0.0010000 sd technical 10 1 0.1750000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E415 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 1200 Yes No NA 300 C079 NA 10 1 ng/g 0.4070000 NA 0.0010000 sd technical 10 1 0.0900000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E416 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 600 No No NA NA C080 NA 10 1 ng/g 0.4070000 NA 0.0010000 sd technical 10 1 0.3110000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E417 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 900 No No NA NA C081 NA 10 1 ng/g 0.4070000 NA 0.0010000 sd technical 10 1 0.2840000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E418 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 1200 No No NA NA C082 NA 10 1 ng/g 0.4070000 NA 0.0010000 sd technical 10 1 0.0940000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E419 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300 C083 NA 10 1 ng/g 0.4070000 NA 0.0010000 sd technical 10 1 0.2970000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E420 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300 C084 NA 10 1 ng/g 0.4070000 NA 0.0010000 sd technical 10 1 0.1610000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E421 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300 C085 NA 10 1 ng/g 0.4070000 NA 0.0010000 sd technical 10 1 0.0850000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E422 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300 C086 NA 10 1 ng/g 0.4070000 NA 0.0010000 sd technical 10 1 0.1640000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E423 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300 C087 NA 10 1 ng/g 0.4070000 NA 0.0010000 sd technical 10 1 0.0930000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E424 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300 C088 NA 10 1 ng/g 0.4070000 NA 0.0010000 sd technical 10 1 0.0670000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E425 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 600 Yes No NA 300 C089 NA 10 1 ng/g 0.2980000 NA 0.0010000 sd technical 10 1 0.1970000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E426 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 900 Yes No NA 300 C090 NA 10 1 ng/g 0.2980000 NA 0.0010000 sd technical 10 1 0.1460000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E427 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 1200 Yes No NA 300 C091 NA 10 1 ng/g 0.2980000 NA 0.0010000 sd technical 10 1 0.0900000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E428 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 600 No No NA NA C092 NA 10 1 ng/g 0.2980000 NA 0.0010000 sd technical 10 1 0.2120000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E429 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 900 No No NA NA C093 NA 10 1 ng/g 0.2980000 NA 0.0010000 sd technical 10 1 0.1220000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E430 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 1200 No No NA NA C094 NA 10 1 ng/g 0.2980000 NA 0.0010000 sd technical 10 1 0.0940000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E431 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300 C095 NA 10 1 ng/g 0.2980000 NA 0.0010000 sd technical 10 1 0.1470000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E432 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300 C096 NA 10 1 ng/g 0.2980000 NA 0.0010000 sd technical 10 1 0.1280000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E433 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300 C097 NA 10 1 ng/g 0.2980000 NA 0.0010000 sd technical 10 1 0.0690000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E434 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300 C098 NA 10 1 ng/g 0.2980000 NA 0.0010000 sd technical 10 1 0.1450000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E435 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300 C099 NA 10 1 ng/g 0.2980000 NA 0.0010000 sd technical 10 1 0.1020000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E436 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300 C100 NA 10 1 ng/g 0.2980000 NA 0.0010000 sd technical 10 1 0.0420000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E437 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 600 Yes No NA 300 C089 NA 10 1 ng/g 0.4180000 NA 0.0010000 sd technical 10 1 0.3720000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E438 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 900 Yes No NA 300 C090 NA 10 1 ng/g 0.4180000 NA 0.0010000 sd technical 10 1 0.2510000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E439 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 1200 Yes No NA 300 C091 NA 10 1 ng/g 0.4180000 NA 0.0010000 sd technical 10 1 0.0940000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E440 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 600 No No NA NA C092 NA 10 1 ng/g 0.4180000 NA 0.0010000 sd technical 10 1 0.2540000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E441 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 900 No No NA NA C093 NA 10 1 ng/g 0.4180000 NA 0.0010000 sd technical 10 1 0.1800000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E442 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 1200 No No NA NA C094 NA 10 1 ng/g 0.4180000 NA 0.0010000 sd technical 10 1 0.0970000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E443 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300 C095 NA 10 1 ng/g 0.4180000 NA 0.0010000 sd technical 10 1 0.3260000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E444 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300 C096 NA 10 1 ng/g 0.4180000 NA 0.0010000 sd technical 10 1 0.1550000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E445 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300 C097 NA 10 1 ng/g 0.4180000 NA 0.0010000 sd technical 10 1 0.0630000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E446 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300 C098 NA 10 1 ng/g 0.4180000 NA 0.0010000 sd technical 10 1 0.3580000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E447 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300 C099 NA 10 1 ng/g 0.4180000 NA 0.0010000 sd technical 10 1 0.1970000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E448 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300 C100 NA 10 1 ng/g 0.4180000 NA 0.0010000 sd technical 10 1 0.0560000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E449 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 600 Yes No NA 300 C101 NA 10 1 ng/g 0.1530000 NA 0.0010000 sd technical 10 1 0.1470000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E450 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 900 Yes No NA 300 C102 NA 10 1 ng/g 0.1530000 NA 0.0010000 sd technical 10 1 0.1150000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E451 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 1200 Yes No NA 300 C103 NA 10 1 ng/g 0.1530000 NA 0.0010000 sd technical 10 1 0.0500000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E452 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 600 No No NA NA C104 NA 10 1 ng/g 0.1530000 NA 0.0010000 sd technical 10 1 0.1480000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E453 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 900 No No NA NA C105 NA 10 1 ng/g 0.1530000 NA 0.0010000 sd technical 10 1 0.1070000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E454 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOA 8 NA Yes Baking oil-based NA 160 1200 No No NA 300 C106 NA 10 1 ng/g 0.1530000 NA 0.0010000 sd technical 10 1 0.0570000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E455 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300 C107 NA 10 1 ng/g 0.1530000 NA 0.0010000 sd technical 10 1 0.1210000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E456 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300 C108 NA 10 1 ng/g 0.1530000 NA 0.0010000 sd technical 10 1 0.0950000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E457 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300 C109 NA 10 1 ng/g 0.1530000 NA 0.0010000 sd technical 10 1 0.0430000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E458 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300 C110 NA 10 1 ng/g 0.1530000 NA 0.0010000 sd technical 10 1 0.1150000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E459 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300 C111 NA 10 1 ng/g 0.1530000 NA 0.0010000 sd technical 10 1 0.0820000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E460 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300 C112 NA 10 1 ng/g 0.1530000 NA 0.0010000 sd technical 10 1 0.0330000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E461 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 600 Yes No NA 300 C101 NA 10 1 ng/g 0.7860000 NA 0.0010000 sd technical 10 1 0.6640000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E462 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 900 Yes No NA 300 C102 NA 10 1 ng/g 0.7860000 NA 0.0010000 sd technical 10 1 0.3120000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E463 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 1200 Yes No NA 300 C103 NA 10 1 ng/g 0.7860000 NA 0.0010000 sd technical 10 1 0.0990000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E464 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 600 No No NA NA C104 NA 10 1 ng/g 0.7860000 NA 0.0010000 sd technical 10 1 0.6180000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E465 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 900 No No NA NA C105 NA 10 1 ng/g 0.7860000 NA 0.0010000 sd technical 10 1 0.3780000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E466 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 1200 No No NA NA C106 NA 10 1 ng/g 0.7860000 NA 0.0010000 sd technical 10 1 0.1070000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E467 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300 C107 NA 10 1 ng/g 0.7860000 NA 0.0010000 sd technical 10 1 0.5980000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E468 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300 C108 NA 10 1 ng/g 0.7860000 NA 0.0010000 sd technical 10 1 0.4020000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E469 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300 C109 NA 10 1 ng/g 0.7860000 NA 0.0010000 sd technical 10 1 0.0970000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E470 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300 C110 NA 10 1 ng/g 0.7860000 NA 0.0010000 sd technical 10 1 0.6180000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E471 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300 C111 NA 10 1 ng/g 0.7860000 NA 0.0010000 sd technical 10 1 0.2460000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E472 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300 C112 NA 10 1 ng/g 0.7860000 NA 0.0010000 sd technical 10 1 0.0890000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E473 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 600 Yes No NA 300 C113 NA 10 1 ng/g 0.1080000 NA 0.0010000 sd technical 10 1 0.0980000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E474 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 900 Yes No NA 300 C114 NA 10 1 ng/g 0.1080000 NA 0.0010000 sd technical 10 1 0.0620000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E475 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 1200 Yes No NA 300 C115 NA 10 1 ng/g 0.1080000 NA 0.0010000 sd technical 10 1 0.0430000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E476 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 600 No No NA NA C116 NA 10 1 ng/g 0.1080000 NA 0.0010000 sd technical 10 1 0.0800000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E477 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 900 No No NA NA C117 NA 10 1 ng/g 0.1080000 NA 0.0010000 sd technical 10 1 0.0600000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E478 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 1200 No No NA NA C118 NA 10 1 ng/g 0.1080000 NA 0.0010000 sd technical 10 1 0.0450000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E479 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300 C119 NA 10 1 ng/g 0.1080000 NA 0.0010000 sd technical 10 1 0.0980000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E480 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300 C120 NA 10 1 ng/g 0.1080000 NA 0.0010000 sd technical 10 1 0.0700000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E481 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300 C121 NA 10 1 ng/g 0.1080000 NA 0.0010000 sd technical 10 1 0.0340000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E482 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300 C122 NA 10 1 ng/g 0.1080000 NA 0.0010000 sd technical 10 1 0.0650000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E483 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300 C123 NA 10 1 ng/g 0.1080000 NA 0.0010000 sd technical 10 1 0.0580000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E484 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300 C124 NA 10 1 ng/g 0.1080000 NA 0.0010000 sd technical 10 1 0.0320000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E485 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 600 Yes No NA 300 C113 NA 10 1 ng/g 0.2740000 NA 0.0010000 sd technical 10 1 0.1540000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E486 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 900 Yes No NA 300 C114 NA 10 1 ng/g 0.2740000 NA 0.0010000 sd technical 10 1 0.1080000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E487 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 1200 Yes No NA 300 C115 NA 10 1 ng/g 0.2740000 NA 0.0010000 sd technical 10 1 0.0920000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E488 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 600 No No NA NA C116 NA 10 1 ng/g 0.2740000 NA 0.0010000 sd technical 10 1 0.1470000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E489 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 900 No No NA NA C117 NA 10 1 ng/g 0.2740000 NA 0.0010000 sd technical 10 1 0.1020000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E490 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 1200 No No NA NA C118 NA 10 1 ng/g 0.2740000 NA 0.0010000 sd technical 10 1 0.0940000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E491 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300 C119 NA 10 1 ng/g 0.2740000 NA 0.0010000 sd technical 10 1 0.1260000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E492 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300 C120 NA 10 1 ng/g 0.2740000 NA 0.0010000 sd technical 10 1 0.0990000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E493 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300 C121 NA 10 1 ng/g 0.2740000 NA 0.0010000 sd technical 10 1 0.0520000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E494 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300 C122 NA 10 1 ng/g 0.2740000 NA 0.0010000 sd technical 10 1 0.1020000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E495 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300 C123 NA 10 1 ng/g 0.2740000 NA 0.0010000 sd technical 10 1 0.0760000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E496 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300 C124 NA 10 1 ng/g 0.2740000 NA 0.0010000 sd technical 10 1 0.0490000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E497 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 600 Yes No NA 300 C125 NA 10 1 ng/g 0.1810000 NA 0.0010000 sd technical 10 1 0.1450000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E498 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 900 Yes No NA 300 C126 NA 10 1 ng/g 0.1810000 NA 0.0010000 sd technical 10 1 0.1130000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E499 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 1200 Yes No NA 300 C127 NA 10 1 ng/g 0.1810000 NA 0.0010000 sd technical 10 1 0.0540000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E500 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 600 No No NA NA C128 NA 10 1 ng/g 0.1810000 NA 0.0010000 sd technical 10 1 0.1520000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E501 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 900 No No NA NA C129 NA 10 1 ng/g 0.1810000 NA 0.0010000 sd technical 10 1 0.1280000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E502 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 1200 No No NA NA C130 NA 10 1 ng/g 0.1810000 NA 0.0010000 sd technical 10 1 0.0610000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E503 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300 C131 NA 10 1 ng/g 0.1810000 NA 0.0010000 sd technical 10 1 0.1220000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E504 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300 C132 NA 10 1 ng/g 0.1810000 NA 0.0010000 sd technical 10 1 0.0920000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E505 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300 C133 NA 10 1 ng/g 0.1810000 NA 0.0010000 sd technical 10 1 0.0490000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E506 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300 C134 NA 10 1 ng/g 0.1810000 NA 0.0010000 sd technical 10 1 0.1180000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E507 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300 C135 NA 10 1 ng/g 0.1810000 NA 0.0010000 sd technical 10 1 0.0890000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E508 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300 C136 NA 10 1 ng/g 0.1810000 NA 0.0010000 sd technical 10 1 0.0440000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E509 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 600 Yes No NA 300 C125 NA 10 1 ng/g 0.4760000 NA 0.0010000 sd technical 10 1 0.3570000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E510 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 900 Yes No NA 300 C126 NA 10 1 ng/g 0.4760000 NA 0.0010000 sd technical 10 1 0.2100000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E511 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 1200 Yes No NA 300 C127 NA 10 1 ng/g 0.4760000 NA 0.0010000 sd technical 10 1 0.0920000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E512 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 600 No No NA NA C128 NA 10 1 ng/g 0.4760000 NA 0.0010000 sd technical 10 1 0.2560000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E513 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 900 No No NA NA C129 NA 10 1 ng/g 0.4760000 NA 0.0010000 sd technical 10 1 0.1840000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E514 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 1200 No No NA NA C130 NA 10 1 ng/g 0.4760000 NA 0.0010000 sd technical 10 1 0.0990000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E515 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300 C131 NA 10 1 ng/g 0.4760000 NA 0.0010000 sd technical 10 1 0.3440000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E516 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300 C132 NA 10 1 ng/g 0.4760000 NA 0.0010000 sd technical 10 1 0.1480000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E517 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300 C133 NA 10 1 ng/g 0.4760000 NA 0.0010000 sd technical 10 1 0.0820000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E518 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300 C134 NA 10 1 ng/g 0.4760000 NA 0.0010000 sd technical 10 1 0.3410000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E519 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300 C135 NA 10 1 ng/g 0.4760000 NA 0.0010000 sd technical 10 1 0.1920000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F010 Sungur_2019 2019 Turkey E520 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300 C136 NA 10 1 ng/g 0.4760000 NA 0.0010000 sd technical 10 1 0.0540000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA
F011 Taylor_2019 2019 Australia E521 Dusky flathead Platycephalus fuscus vertebrate marine fish 21.470000 PFHxS 6 linear Yes Baking oil-based NA 75 600 No Yes olive oil 20 C137 Contaminated site 4 4 ng/g 0.9673000 NA 1.0026000 sd biological 4 4 1.4745000 NA 1.7430000 biological 1 ng/g 0.023508736 0.078362453 Shared control Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied ML - check empty fields, why SE/SD field is NA? 1.0026000 1.7430000
F011 Taylor_2019 2019 Australia E522 Dusky flathead Platycephalus fuscus vertebrate marine fish 21.470000 PFOS 8 linear Yes Baking oil-based NA 75 600 No Yes olive oil 20 C137 Contaminated site 6 6 ng/g 75.6360000 NA 133.7000000 sd biological 6 6 84.5499000 NA 130.5000000 biological 1 ng/g 0.023185477 0.077284922 Shared control Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 133.7000000 130.5000000
F011 Taylor_2019 2019 Australia E523 Dusky flathead Platycephalus fuscus vertebrate marine fish 21.470000 PFOS 8 linear Yes Baking oil-based NA 75 600 No Yes olive oil 20 C138 Clean site 3 3 ng/g 0.0894000 NA 0.0339000 sd biological 3 3 0.1210000 NA 0.0390000 biological 1 ng/g 0.023185477 0.077284922 Shared control Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.0339000 0.0390000
F011 Taylor_2019 2019 Australia E526 Dusky flathead Platycephalus fuscus vertebrate marine fish 21.470000 PFDS 10 linear Yes Baking oil-based NA 75 600 No Yes olive oil 20 C137 Contaminated site 2 2 ng/g 0.1391000 NA 0.0247000 sd biological 2 2 0.3760000 NA 0.0240000 biological 1 ng/g 0.030122517 0.10040839 Shared control Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.0247000 0.0240000
F011 Taylor_2019 2019 Australia E527 Dusky flathead Platycephalus fuscus vertebrate marine fish 21.470000 FOSA 8 NA Yes Baking oil-based NA 75 600 No Yes olive oil 20 C137 Contaminated site 2 2 ng/g 0.0749000 <LOQ NA Not available bacause Mc/Me is below LOD/LOQ NA 2 2 0.1985000 NA 0.0120000 biological 1 ng/g 0.034582913 0.115276378 Shared control Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA NA 0.0120000
F011 Taylor_2019 2019 Australia E528 Dusky flathead Platycephalus fuscus vertebrate marine fish 18.640000 PFHxS 6 linear Yes Frying oil-based NA 82 120 No Yes olive oil 40 C140 Contaminated site 5 5 ng/g 0.7841000 NA 0.9602000 sd biological 5 5 0.8414000 NA 1.0420000 biological 1 ng/g 0.023508736 0.078362453 Shared control Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.9602000 1.0420000
F011 Taylor_2019 2019 Australia E529 Dusky flathead Platycephalus fuscus vertebrate marine fish 18.640000 PFOS 8 linear Yes Frying oil-based NA 82 120 No Yes olive oil 40 C139 Contaminated site 6 6 ng/g 75.6360000 NA 133.7000000 sd biological 6 6 70.8427000 NA 106.0000000 biological 1 ng/g 0.023185477 0.077284922 Shared control Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 133.7000000 106.0000000
F011 Taylor_2019 2019 Australia E530 Dusky flathead Platycephalus fuscus vertebrate marine fish 18.640000 PFOS 8 linear Yes Frying oil-based NA 82 120 No Yes olive oil 40 C140 Clean site 2 2 ng/g 0.1090000 NA 0.0014000 sd biological 2 2 0.2005000 NA 0.0730000 biological 1 ng/g 0.023185477 0.077284922 Shared control Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.0014000 0.0730000
F011 Taylor_2019 2019 Australia E533 Dusky flathead Platycephalus fuscus vertebrate marine fish 18.640000 FOSA 8 NA Yes Frying oil-based NA 82 120 No Yes olive oil 40 C139 Contaminated site 4 4 ng/g 0.1070000 NA 0.0397000 sd biological 4 4 0.2540000 NA 0.1320000 biological 1 ng/g 0.034582913 0.115276378 Shared control Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.0397000 0.1320000
F011 Taylor_2019 2019 Australia E534 Blue swimmer crab Portunus armatus invertebrate crustacea NA PFHxA 6 linear No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500 C141 Contaminated site 3 3 ng/g 0.1513000 NA 0.0306000 sd biological 3 3 0.0729200 NA 0.0210000 biological 1 ng/g 0.028099467 0.093664888 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.0306000 0.0210000
F011 Taylor_2019 2019 Australia E535 Blue swimmer crab Portunus armatus invertebrate crustacea NA PFHpA 7 NA No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500 C141 Contaminated site 6 6 ng/g 0.2070000 NA 0.1445000 sd biological 6 6 0.1086500 NA 0.0520000 biological 1 ng/g 0.01867491 0.0622497 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.1445000 0.0520000
F011 Taylor_2019 2019 Australia E536 Blue swimmer crab Portunus armatus invertebrate crustacea NA PFOA 8 NA No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500 C141 Contaminated site 6 6 ng/g 0.4279000 NA 0.2601000 sd biological 6 6 0.2316000 NA 0.1070000 biological 1 ng/g 0.014519809 0.048399364 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.2601000 0.1070000
F011 Taylor_2019 2019 Australia E537 Blue swimmer crab Portunus armatus invertebrate crustacea NA PFOA 8 NA No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500 C142 Clean site 4 4 ng/g 0.0433000 NA 0.0137000 sd biological 4 4 0.0712200 NA 0.0660000 biological 1 ng/g 0.014519809 0.048399364 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.0137000 0.0660000
F011 Taylor_2019 2019 Australia E538 Blue swimmer crab Portunus armatus invertebrate crustacea NA PFUnDA 11 NA No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500 C141 Contaminated site 4 4 ng/g 0.1128000 NA 0.0093000 sd biological 4 4 0.0579700 <LOQ NA NA 1 ng/g 0.026755217 0.089184057 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.0093000 NA
F011 Taylor_2019 2019 Australia E539 Blue swimmer crab Portunus armatus invertebrate crustacea NA PFUnDA 11 NA No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500 C142 Clean site 1 1 ng/g 0.1047000 NA NA sd biological 1 1 0.0579700 <LOQ NA NA 1 ng/g 0.026755217 0.089184057 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA NA NA
F011 Taylor_2019 2019 Australia E540 Blue swimmer crab Portunus armatus invertebrate crustacea NA PFDoDA 12 NA No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500 C141 Contaminated site 1 1 ng/g 0.0802000 <LOQ NA Not available bacause Mc/Me is below LOD/LOQ NA 1 1 0.1279700 No sd, as N = 1 NA NA 1 ng/g 0.037026547 0.123421824 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA NA NA
F011 Taylor_2019 2019 Australia E541 Blue swimmer crab Portunus armatus invertebrate crustacea NA PFDoDA 12 NA No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500 C142 Clean site 1 1 ng/g 0.1230000 NA NA sd biological 1 1 0.0802200 <LOQ NA NA 1 ng/g 0.037026547 0.123421824 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA NA NA
F011 Taylor_2019 2019 Australia E542 Blue swimmer crab Portunus armatus invertebrate crustacea NA PFHxS 6 linear No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500 C141 Contaminated site 6 6 ng/g 0.5991000 NA 0.2053000 sd biological 6 6 0.3865700 NA 0.0790000 biological 1 ng/g 0.023508736 0.078362453 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.2053000 0.0790000
F011 Taylor_2019 2019 Australia E543 Blue swimmer crab Portunus armatus invertebrate crustacea NA PFHxS 6 linear No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500 C142 Clean site 1 1 ng/g 0.1230000 <LOQ NA Not available bacause Mc/Me is below LOD/LOQ NA 1 1 0.0809900 No sd, as N = 1 NA NA 1 ng/g 0.023508736 0.078362453 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA NA NA
F011 Taylor_2019 2019 Australia E544 Blue swimmer crab Portunus armatus invertebrate crustacea NA PFOS 8 linear No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500 C141 Contaminated site 6 6 ng/g 5.0500000 NA 0.4637000 sd biological 6 6 5.5333300 NA 0.8290000 biological 1 ng/g 0.023185477 0.077284922 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.4637000 0.8290000
F011 Taylor_2019 2019 Australia E545 Blue swimmer crab Portunus armatus invertebrate crustacea NA PFOS 8 linear No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500 C142 Clean site 6 6 ng/g 0.1917000 NA 0.2129000 sd biological 6 6 0.1917100 NA 0.2360000 biological 1 ng/g 0.023185477 0.077284922 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.2129000 0.2360000
F011 Taylor_2019 2019 Australia E548 Blue swimmer crab Portunus armatus invertebrate crustacea NA FOSA 8 NA No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500 C141 Contaminated site 6 6 ng/g 0.3112000 NA 0.1413000 sd biological 6 6 0.3215300 NA 0.0990000 biological 1 ng/g 0.034582913 0.115276378 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.1413000 0.0990000
F011 Taylor_2019 2019 Australia E549 School prawn Metapenaeus macleayi invertebrate crustacea NA PFHpA 7 NA Yes Boiling water-based in saltwater (8.5 g/L) 100 240 Yes No NA 500 C143 Contaminated site 10 1 ng/g 0.0802000 <LOQ NA Not available bacause Mc/Me is below LOD/LOQ NA 10 1 0.1279700 NA NA biological 1 ng/g 0.01867491 0.0622497 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA NA NA
F011 Taylor_2019 2019 Australia E550 School prawn Metapenaeus macleayi invertebrate crustacea NA PFOA 8 NA Yes Boiling water-based in saltwater (8.5 g/L) 100 240 Yes No NA 500 C143 Contaminated site 60 6 ng/g 0.2229000 NA 0.0668000 sd biological 60 6 0.4689700 NA 0.1040000 biological 1 ng/g 0.014519809 0.048399364 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.0668000 0.1040000
F011 Taylor_2019 2019 Australia E551 School prawn Metapenaeus macleayi invertebrate crustacea NA PFNA 9 NA Yes Boiling water-based in saltwater (8.5 g/L) 100 240 Yes No NA 500 C143 Contaminated site 60 6 ng/g 0.0910000 <LOQ NA Not available bacause Mc/Me is below LOD/LOQ NA 60 6 0.2330900 NA 0.0370000 biological 1 ng/g 0.036013573 0.120045244 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA NA 0.0370000
F011 Taylor_2019 2019 Australia E552 School prawn Metapenaeus macleayi invertebrate crustacea NA PFDA 10 NA Yes Boiling water-based in saltwater (8.5 g/L) 100 240 Yes No NA 500 C143 Contaminated site 50 5 ng/g 0.0854000 <LOQ NA Not available bacause Mc/Me is below LOD/LOQ NA 50 5 0.1877100 NA 0.0530000 biological 1 ng/g 0.039417906 0.131393021 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA NA 0.0530000
F011 Taylor_2019 2019 Australia E553 School prawn Metapenaeus macleayi invertebrate crustacea NA PFHxS 6 linear Yes Boiling water-based in saltwater (8.5 g/L) 100 240 Yes No NA 500 C143 Contaminated site 60 6 ng/g 2.3305000 NA 1.3905000 sd biological 60 6 6.3161900 NA 1.6280000 biological 1 ng/g 0.023508736 0.078362453 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 1.3905000 1.6280000
F011 Taylor_2019 2019 Australia E554 School prawn Metapenaeus macleayi invertebrate crustacea NA PFOS 8 linear Yes Boiling water-based in saltwater (8.5 g/L) 100 240 Yes No NA 500 C143 Contaminated site 60 6 ng/g 7.4167000 NA 2.8414000 sd biological 60 6 16.1667000 NA 3.8690000 biological 1 ng/g 0.023185477 0.077284922 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 2.8414000 3.8690000
F011 Taylor_2019 2019 Australia E555 School prawn Metapenaeus macleayi invertebrate crustacea NA PFOS 8 linear Yes Boiling water-based in saltwater (8.5 g/L) 100 240 Yes No NA 500 C144 Clean site 50 5 ng/g 0.0562000 NA 0.0133000 sd biological 50 5 0.1180000 NA 0.0290000 biological 1 ng/g 0.023185477 0.077284922 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.0133000 0.0290000
F013 Vassiliadou_2015 2015 Greece E557 Anchovy Engraulis encrasicolus vertebrate marine fish 72.739187 PFUnDA 11 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300 C145 NA 30 1 ng/g 1.5000000 NA 0.0400000 sd technical 30 1 1.7500000 NA 0.0500000 technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied ML - why “Se_technical_biological” is coded as “sd”? “If_technical_how_many” needs a number. Shared control between differend cooking methods NA NA
F013 Vassiliadou_2015 2015 Greece E558 Anchovy Engraulis encrasicolus vertebrate marine fish 72.739187 PFDoDA 12 NA No Frying oil-based NA 170 NA No Yes olive oil 300 C145 NA 30 1 ng/g 1.8600000 NA 0.1900000 sd technical 30 1 2.9900000 NA 0.2200000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E559 Anchovy Engraulis encrasicolus vertebrate marine fish 72.739187 PFOS 8 linear Yes Frying oil-based NA 170 NA No Yes olive oil 300 C145 NA 30 1 ng/g 3.0600000 NA 0.1000000 sd technical 30 1 6.6200000 NA 0.1400000 technical 1 ng/g 0.49 1.48 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E560 Bogue Boops boops vertebrate marine fish 18.354430 PFUnDA 11 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300 C146 NA 30 1 ng/g 0.2400000 NA 0.0300000 sd technical 30 1 0.4400000 NA 0.0200000 technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E561 Bogue Boops boops vertebrate marine fish 18.354430 PFDoDA 12 NA No Frying oil-based NA 170 NA No Yes olive oil 300 C146 NA 30 1 ng/g 0.5600000 NA 0.0800000 sd technical 30 1 1.1200000 NA 0.0300000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E562 Bogue Boops boops vertebrate marine fish 18.354430 PFOS 8 linear Yes Frying oil-based NA 170 NA No Yes olive oil 300 C146 NA 30 1 ng/g 0.8200000 NA 0.0400000 sd technical 30 1 1.2700000 NA 0.0600000 technical 1 ng/g 0.49 1.48 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E563 Hake Merluccius merluccius vertebrate marine fish 36.000000 PFUnDA 11 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300 C147 NA 10 1 ng/g 0.4200000 NA 0.0500000 sd technical 10 1 0.7000000 LOD NA technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E564 Hake Merluccius merluccius vertebrate marine fish 36.000000 PFDoDA 12 NA No Frying oil-based NA 170 NA No Yes olive oil 300 C147 NA 10 1 ng/g 0.6200000 NA 0.0800000 sd technical 10 1 0.1000000 <LOD NA NA 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E565 Hake Merluccius merluccius vertebrate marine fish 36.000000 PFBS 4 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300 C147 NA 10 1 ng/g 0.4500000 NA 0.0700000 sd technical 10 1 0.8300000 NA 0.0300000 technical 1 ng/g 0.57 1.7 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E566 Hake Merluccius merluccius vertebrate marine fish 36.000000 PFOS 8 linear Yes Frying oil-based NA 170 NA No Yes olive oil 300 C147 NA 10 1 ng/g 0.8400000 NA 0.1000000 sd technical 10 1 1.2400000 NA 0.0600000 technical 1 ng/g 0.49 1.48 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E567 Picarel Spicara smaris vertebrate marine fish 44.037940 PFUnDA 11 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300 C148 NA 30 1 ng/g 0.7000000 NA 0.0900000 sd technical 30 1 1.3500000 NA 0.0800000 technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E568 Picarel Spicara smaris vertebrate marine fish 44.037940 PFOS 8 linear Yes Frying oil-based NA 170 NA No Yes olive oil 300 C148 NA 30 1 ng/g 20.3700000 NA 2.4700000 sd technical 30 1 44.6900000 NA 3.9300000 technical 1 ng/g 0.49 1.48 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E569 Sand smelt Atherina boyeri vertebrate marine fish 79.108280 PFUnDA 11 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300 C149 NA 30 1 ng/g 0.3500000 <LOD NA Not available bacause Mc/Me is below LOD/LOQ NA 30 1 0.7400000 NA 0.0900000 technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E570 Sand smelt Atherina boyeri vertebrate marine fish 79.108280 PFDoDA 12 NA No Frying oil-based NA 170 NA No Yes olive oil 300 C149 NA 30 1 ng/g 1.0800000 NA 0.0300000 sd technical 30 1 1.9800000 NA 0.0400000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E571 Sand smelt Atherina boyeri vertebrate marine fish 79.108280 PFOS 8 linear Yes Frying oil-based NA 170 NA No Yes olive oil 300 C149 NA 30 1 ng/g 1.1600000 NA 0.0500000 sd technical 30 1 3.0100000 NA 0.1300000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E572 Sardine Sardina pilchardus vertebrate marine fish 57.258065 PFDoDA 12 NA No Frying oil-based NA 170 NA No Yes olive oil 300 C150 NA 30 1 ng/g 0.1000000 <LOD NA Not available bacause Mc/Me is below LOD/LOQ NA 30 1 0.9300000 NA 0.0300000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E573 Striped mullet Mullus barbatus vertebrate marine fish 61.316212 PFNA 9 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300 C151 NA 30 1 ng/g 0.6000000 NA 0.0300000 sd technical 30 1 0.5700000 NA 0.1100000 technical 1 ng/g 0.42 1.25 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E574 Striped mullet Mullus barbatus vertebrate marine fish 61.316212 PFDA 10 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300 C151 NA 30 1 ng/g 0.6500000 NA 0.0600000 sd technical 30 1 0.5600000 NA 0.0700000 technical 1 ng/g 0.69 2.08 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E575 Striped mullet Mullus barbatus vertebrate marine fish 61.316212 PFUnDA 11 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300 C151 NA 30 1 ng/g 1.0500000 NA 0.1300000 sd technical 30 1 0.7300000 NA 0.2000000 technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E576 Striped mullet Mullus barbatus vertebrate marine fish 61.316212 PFDoDA 12 NA No Frying oil-based NA 170 NA No Yes olive oil 300 C151 NA 30 1 ng/g 0.1000000 <LOD NA Not available bacause Mc/Me is below LOD/LOQ technical 30 1 1.3800000 NA 0.0700000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E577 Striped mullet Mullus barbatus vertebrate marine fish 61.316212 PFOS 8 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300 C151 NA 30 1 ng/g 5.6600000 NA 0.1500000 sd technical 30 1 0.1000000 <LOD NA NA 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E578 Shrimp Parapenaeus longirostris vertebrate marine fish NA PFPeA 5 NA No Frying oil-based NA 170 NA No Yes olive oil 300 C152 NA 40 1 ng/g 4.9400000 NA 0.2600000 sd technical 40 1 14.8800000 NA 1.6100000 technical 1 ng/g 0.39 1.17 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E579 Shrimp Parapenaeus longirostris vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300 C152 NA 40 1 ng/g 0.3000000 <LOD NA Not available bacause Mc/Me is below LOD/LOQ technical 40 1 0.9900000 NA 0.2100000 technical 1 ng/g 0.6 1.82 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E580 Shrimp Parapenaeus longirostris vertebrate marine fish NA PFNA 9 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300 C152 NA 40 1 ng/g 1.2700000 NA 0.0700000 sd technical 40 1 1.5200000 NA 0.1100000 technical 1 ng/g 0.42 1.25 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E581 Shrimp Parapenaeus longirostris vertebrate marine fish NA PFDA 10 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300 C152 NA 40 1 ng/g 1.7300000 NA 0.0800000 sd technical 40 1 1.8100000 NA 0.1900000 technical 1 ng/g 0.69 2.08 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E582 Shrimp Parapenaeus longirostris vertebrate marine fish NA PFUnDA 11 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300 C152 NA 40 1 ng/g 2.7600000 NA 0.2100000 sd technical 40 1 6.8200000 NA 0.2200000 technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E583 Shrimp Parapenaeus longirostris vertebrate marine fish NA PFDoDA 12 NA No Frying oil-based NA 170 NA No Yes olive oil 300 C152 NA 40 1 ng/g 1.3600000 NA 0.0900000 sd technical 40 1 2.3100000 NA 0.0900000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E584 Shrimp Parapenaeus longirostris vertebrate marine fish NA PFBS 4 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300 C152 NA 40 1 ng/g 1.3700000 NA 0.1600000 sd technical 40 1 0.2850000 <LOD NA NA 1 ng/g 0.57 1.7 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E585 Shrimp Parapenaeus longirostris vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based NA 170 NA No Yes olive oil 300 C152 NA 40 1 ng/g 5.1500000 NA 0.3900000 sd technical 40 1 8.0200000 NA 0.4200000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E586 Squid Loligo vulgaris vertebrate marine fish 47.867299 PFPeA 5 NA No Frying oil-based NA 170 NA No Yes olive oil 300 C153 NA 40 1 ng/g 0.1950000 <LOD NA sd technical 40 1 5.0600000 NA 0.1900000 technical 1 ng/g 0.39 1.17 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E587 Squid Loligo vulgaris vertebrate marine fish 47.867299 PFDA 10 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300 C153 NA 40 1 ng/g 0.3450000 <LOD NA sd technical 40 1 0.5100000 NA 0.0400000 technical 1 ng/g 0.69 2.08 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E588 Squid Loligo vulgaris vertebrate marine fish 47.867299 PFUnDA 11 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300 C153 NA 40 1 ng/g 0.3500000 <LOD NA sd technical 40 1 1.0400000 NA 0.0200000 technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E589 Squid Loligo vulgaris vertebrate marine fish 47.867299 PFDoDA 12 NA No Frying oil-based NA 170 NA No Yes olive oil 300 C153 NA 40 1 ng/g 0.1000000 <LOD NA Not available bacause Mc/Me is below LOD/LOQ NA 40 1 1.6500000 NA 0.0700000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E590 Squid Loligo vulgaris vertebrate marine fish 47.867299 PFOS 8 linear Yes Frying oil-based NA 170 NA No Yes olive oil 300 C153 NA 40 1 ng/g 0.1000000 <LOD NA sd technical 40 1 1.5600000 NA 0.1700000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E591 Anchovy Engraulis encrasicolus vertebrate marine fish 33.158585 PFDA 10 NA Yes Grilling No liquid NA 180 NA No No NA NA C154 NA 30 1 ng/g 0.3450000 <LOD NA sd technical 30 1 0.8300000 NA 0.0100000 technical 1 ng/g 0.69 2.08 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E592 Anchovy Engraulis encrasicolus vertebrate marine fish 33.158585 PFUnDA 11 NA Yes Grilling No liquid NA 180 NA No No NA NA C154 NA 30 1 ng/g 1.5000000 NA 0.0400000 sd technical 30 1 2.7300000 NA 0.1300000 technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E593 Anchovy Engraulis encrasicolus vertebrate marine fish 33.158585 PFDoDA 12 NA No Grilling No liquid NA 180 NA No No NA NA C154 NA 30 1 ng/g 1.8600000 NA 0.1900000 sd technical 30 1 3.5200000 NA 0.1000000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E594 Anchovy Engraulis encrasicolus vertebrate marine fish 33.158585 PFOS 8 linear Yes Grilling No liquid NA 180 NA No No NA NA C154 NA 30 1 ng/g 3.0600000 NA 0.1000000 sd technical 30 1 6.2900000 NA 0.3400000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E595 Bogue Boops boops vertebrate marine fish 7.436709 PFUnDA 11 NA Yes Grilling No liquid NA 180 NA No No NA NA C155 NA 30 1 ng/g 0.2400000 NA 0.0300000 sd technical 30 1 0.4300000 NA 0.0300000 technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E596 Bogue Boops boops vertebrate marine fish 7.436709 PFDoDA 12 NA No Grilling No liquid NA 180 NA No No NA NA C155 NA 30 1 ng/g 0.5600000 NA 0.0800000 sd technical 30 1 0.6300000 NA 0.0200000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E597 Bogue Boops boops vertebrate marine fish 7.436709 PFOS 8 linear Yes Grilling No liquid NA 180 NA No No NA NA C155 NA 30 1 ng/g 0.8200000 NA 0.0400000 sd technical 30 1 0.8700000 NA 0.0700000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E598 Hake Merluccius merluccius vertebrate marine fish 18.909091 PFDA 10 NA Yes Grilling No liquid NA 180 NA No No NA NA C156 NA 10 1 ng/g 0.3450000 <LOD NA Not available bacause Mc/Me is below LOD/LOQ NA 10 1 0.8200000 NA 0.0300000 technical 1 ng/g 0.69 2.08 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E599 Hake Merluccius merluccius vertebrate marine fish 18.909091 PFUnDA 11 NA Yes Grilling No liquid NA 180 NA No No NA NA C156 NA 10 1 ng/g 0.4200000 NA 0.0500000 sd technical 10 1 1.1100000 NA 0.1500000 technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E600 Hake Merluccius merluccius vertebrate marine fish 18.909091 PFDoDA 12 NA No Grilling No liquid NA 180 NA No No NA NA C156 NA 10 1 ng/g 0.6200000 NA 0.0800000 sd technical 10 1 1.8900000 NA 0.0500000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E601 Hake Merluccius merluccius vertebrate marine fish 18.909091 PFBS 4 NA Yes Grilling No liquid NA 180 NA No No NA NA C156 NA 10 1 ng/g 0.4500000 NA 0.0700000 sd technical 10 1 0.2850000 <LOD NA NA 1 ng/g 0.57 1.7 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E602 Hake Merluccius merluccius vertebrate marine fish 18.909091 PFOS 8 linear Yes Grilling No liquid NA 180 NA No No NA NA C156 NA 10 1 ng/g 0.8400000 NA 0.1000000 sd technical 10 1 2.4000000 NA 0.1300000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E603 Sardine Sardina pilchardus vertebrate marine fish 9.946237 PFDA 10 NA Yes Grilling No liquid NA 180 NA No No NA NA C157 NA 30 1 ng/g 0.3450000 <LOD NA Not available bacause Mc/Me is below LOD/LOQ NA 30 1 0.8700000 NA 0.0300000 technical 1 ng/g 0.69 2.08 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E604 Sardine Sardina pilchardus vertebrate marine fish 9.946237 PFUnDA 11 NA Yes Grilling No liquid NA 180 NA No No NA NA C157 NA 30 1 ng/g 0.3500000 <LOD NA sd technical 30 1 1.7000000 NA 0.1300000 technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E605 Sardine Sardina pilchardus vertebrate marine fish 9.946237 PFDoDA 12 NA No Grilling No liquid NA 180 NA No No NA NA C157 NA 30 1 ng/g 0.1000000 <LOD NA sd technical 30 1 3.1900000 NA 0.0900000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E606 Striped mullet Mullus barbatus vertebrate marine fish 17.656501 PFNA 9 NA Yes Grilling No liquid NA 180 NA No No NA NA C158 NA 30 1 ng/g 0.6000000 NA 0.0300000 sd technical 30 1 0.5000000 NA 0.0500000 technical 1 ng/g 0.42 1.25 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E607 Striped mullet Mullus barbatus vertebrate marine fish 17.656501 PFDA 10 NA Yes Grilling No liquid NA 180 NA No No NA NA C158 NA 30 1 ng/g 0.6500000 NA 0.0600000 sd technical 30 1 0.3450000 <LOD NA NA 1 ng/g 0.69 2.08 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E608 Striped mullet Mullus barbatus vertebrate marine fish 17.656501 PFUnDA 11 NA Yes Grilling No liquid NA 180 NA No No NA NA C158 NA 30 1 ng/g 1.0500000 NA 0.1300000 sd technical 30 1 0.8200000 NA 0.0200000 technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E609 Striped mullet Mullus barbatus vertebrate marine fish 17.656501 PFOS 8 linear Yes Grilling No liquid NA 180 NA No No NA NA C158 NA 30 1 ng/g 5.6600000 NA 0.1500000 sd technical 30 1 10.2300000 NA 0.5300000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E610 Squid Loligo vulgaris vertebrate mollusca 24.289099 PFOA 8 NA No Grilling No liquid NA 180 NA No No NA NA C159 NA 40 1 ng/g 0.3000000 <LOD NA Not available bacause Mc/Me is below LOD/LOQ NA 40 1 0.4000000 NA 0.0100000 technical 1 ng/g 0.6 1.82 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E611 Squid Loligo vulgaris vertebrate mollusca 24.289099 PFDoDA 12 NA No Grilling No liquid NA 180 NA No No NA NA C159 NA 40 1 ng/g 0.1000000 <LOD NA sd technical 40 1 1.0900000 NA 0.0200000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied NA NA NA
F013 Vassiliadou_2015 2015 Greece E612 Squid Loligo vulgaris vertebrate mollusca 24.289099 PFOS 8 linear Yes Grilling No liquid NA 180 NA No No NA NA C159 NA 40 1 ng/g 0.1000000 <LOD NA sd technical 40 1 1.1900000 NA 0.1700000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied NA NA NA

Import phylogenetic information and calculate phylogenetic variance-covariance matrix

The phylogenetic tree was generated in the tree_cooked_fish_MA.Rmd document

tree <- read.tree(here("data", "plot_cooked_fish_MA.tre"))  # Import phylogenetic tree (see tree_cooked_fish_MA.Rmd for more details) 

tree <- compute.brlen(tree)  # Generate branch lengths 

cor_tree <- vcv(tree, corr = T)  # Generate phylogenetic variance-covariance matrix 

dat$Phylogeny <- str_replace(dat$Species_Scientific, " ", "_")  # Add the `phylogeny` column to the data frame

colnames(cor_tree) %in% dat$Phylogeny  # Check correspondence between tip names and data frame
##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
plot(tree)

Calculate effect sizes

The average coefficient of variation in PFAS concentration was calculated for each study and treatment, according to Doncaster and Spake (2018) Correction for bias in meta-analysis of little-replicated studies. Methods in Ecology and Evolution; 9:634-644. Then, these values were averaged across studies and used to calculate the lnRR corrected for small sample sizes (for formula, see the lnRR_func above)

aCV2 <- dat %>% 
               group_by(Study_ID) %>%  # Group by study 
                                     summarise(CV2c = mean((SDc/Mc)^2, na.rm = T),  # Calculate the squared coefficient of variation for control and experimental groups
                                               CV2e = mean((SDe/Me)^2, na.rm = T)) %>% 
                                                                                      ungroup() %>% # ungroup 
                                                                                                   summarise(aCV2c = mean(CV2c, na.rm = T), # Mean CV^2 for exp and control groups across studies
                                                                                                             aCV2e = mean(CV2e, na.rm = T)) 

effect <- lnRR_func(Mc = dat$Mc, 
                    Nc = dat$Nc, 
                    Me = dat$Me, 
                    Ne = dat$Ne, 
                    aCV2c = aCV2[[1]], 
                    aCV2e = aCV2[[2]],
                    rho = 0.8)  # Calculate effect sizes

dat <- dat %>% 
             mutate(N_tilde = (Nc*Ne)/(Nc + Ne)) # Calculate the effective sample size

dat <- cbind(dat, effect) # Merge effect sizes with the data frame

VCV_lnRR <- make_VCV_matrix(dat, V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # Because some effect sizes share the same control, we generated a variance-covariance matrix to account for correlated errors (i.e. effectively dividing the weight of the correlated estimates by half)

Distribution of effect sizes

# mean
ggplot(dat, aes(x = lnRR)) + geom_histogram(fill = "salmon", col = "black", binwidth = 0.2) + 
    theme_classic()

# variance
ggplot(dat, aes(x = var_lnRR)) + geom_histogram(fill = "salmon", col = "black", binwidth = 0.05) + 
    theme_classic()

# log variance
ggplot(dat, aes(x = var_lnRR)) + geom_histogram(fill = "salmon", col = "black", binwidth = 0.05) + 
    scale_x_log10() + theme_classic()

Sample sizes

Table of sample sizes

dat %>%
       summarise( # Calculate the number of effect sizes, studies and species for the main categorical variables
                 `Studies` = n_distinct(Study_ID),
                 `Species` = n_distinct(Species_common),
                 `PFAS type` = n_distinct(PFAS_type),
                 `Cohorts` = n_distinct(Cohort_ID),
                 `Effect sizes` = n_distinct(Effect_ID),
    
                 `Effect sizes (Oil-based)` = n_distinct(Effect_ID[Cooking_Category=="oil-based"]),
                 `Studies (Oil-based)` = n_distinct(Study_ID[Cooking_Category=="oil-based"]),
                 `Species (Oil-based)` = n_distinct(Species_common[Cooking_Category=="oil-based"]),

                 `Effect sizes (Water-based)` = n_distinct(Effect_ID[Cooking_Category=="water-based"]),
                 `Studies (Water-based)` = n_distinct(Study_ID[Cooking_Category=="water-based"]),
                 `Species (Water-based)` = n_distinct(Species_common[Cooking_Category=="water-based"]),

                 `Effect sizes (No liquid)` = n_distinct(Effect_ID[Cooking_Category=="No liquid"]),
                 `Studies (No liquid)` = n_distinct(Study_ID[Cooking_Category=="No liquid"]),
                 `Species (No liquid)` = n_distinct(Species_common[Cooking_Category=="No liquid"]),) -> table_sample_sizes

table_sample_sizes<-t(table_sample_sizes)
colnames(table_sample_sizes)<-"n (sample size)"
kable(table_sample_sizes) %>% kable_styling("striped", position="left")
n (sample size)
Studies 10
Species 39
PFAS type 18
Cohorts 153
Effect sizes 512
Effect sizes (Oil-based) 303
Studies (Oil-based) 7
Species (Oil-based) 28
Effect sizes (Water-based) 140
Studies (Water-based) 8
Species (Water-based) 23
Effect sizes (No liquid) 69
Studies (No liquid) 2
Species (No liquid) 14

Summary of the dataset

kable(summary(dat), "html") %>% kable_styling("striped", position = "left") %>% scroll_box(width = "100%", 
    height = "500px")
Study_ID Author_year Publication_year Country_firstAuthor Effect_ID Species_common Species_Scientific Invertebrate_vertebrate Fish_mollusc Moisture_loss_in_percent PFAS_type PFAS_carbon_chain linear_total Choice_of_9 Cooking_method Cooking_Category Comments_cooking Temperature_in_Celsius Length_cooking_time_in_s Water Oil Oil_type Volume_liquid_ml Cohort_ID Cohort_comment Nc Pooled_Nc Unit_PFAS_conc Mc Mc_comment Sc sd Sc_technical_biological Ne Pooled_Ne Me Me_comment Se Se_technical_biological If_technical_how_many Unit_LOD_LOQ LOD LOQ Design DataSource Raw_data_provided General_comments checked SDc SDe Phylogeny N_tilde lnRR var_lnRR
Length:512 Length:512 Min. :2008 Length:512 Length:512 Length:512 Length:512 Length:512 Length:512 Min. : 6.77 Length:512 Min. : 3.000 Length:512 Length:512 Length:512 Length:512 Length:512 Min. : 75.0 Min. : 120.0 Length:512 Length:512 Length:512 Min. : 5 Length:512 Length:512 Min. : 1.00 Min. :1.000 Length:512 Min. : 0.002 Length:512 Min. : 0.0010 Length:512 Length:512 Min. : 1.00 Min. :1.000 Min. : 0.0020 Length:512 Min. : 0.000 Length:512 Min. :1.000 Length:512 Length:512 Length:512 Length:512 Length:512 Length:512 Length:512 Length:512 Min. : 0.0010 Min. : 0.0010 Length:512 Min. : 0.500 Min. :-6.0350 Min. :0.02396
Class :character Class :character 1st Qu.:2014 Class :character Class :character Class :character Class :character Class :character Class :character 1st Qu.:14.45 Class :character 1st Qu.: 8.000 Class :character Class :character Class :character Class :character Class :character 1st Qu.:100.0 1st Qu.: 600.0 Class :character Class :character Class :character 1st Qu.: 11 Class :character Class :character 1st Qu.: 5.00 1st Qu.:1.000 Class :character 1st Qu.: 0.160 Class :character 1st Qu.: 0.0010 Class :character Class :character 1st Qu.: 5.00 1st Qu.:1.000 1st Qu.: 0.0940 Class :character 1st Qu.: 0.001 Class :character 1st Qu.:1.000 Class :character Class :character Class :character Class :character Class :character Class :character Class :character Class :character 1st Qu.: 0.0354 1st Qu.: 0.0585 Class :character 1st Qu.: 2.500 1st Qu.:-0.8778 1st Qu.:0.14375
Mode :character Mode :character Median :2019 Mode :character Mode :character Mode :character Mode :character Mode :character Mode :character Median :18.35 Mode :character Median : 8.000 Mode :character Mode :character Mode :character Mode :character Mode :character Median :160.0 Median : 600.0 Mode :character Mode :character Mode :character Median : 300 Mode :character Mode :character Median :10.00 Median :1.000 Mode :character Median : 0.298 Mode :character Median : 0.0100 Mode :character Mode :character Median :10.00 Median :1.000 Median : 0.2285 Mode :character Median : 0.020 Mode :character Median :3.000 Mode :character Mode :character Mode :character Mode :character Mode :character Mode :character Mode :character Mode :character Median : 0.1580 Median : 0.1461 Mode :character Median : 5.000 Median :-0.1671 Median :0.14375
NA NA Mean :2017 NA NA NA NA NA NA Mean :21.04 NA Mean : 8.994 NA NA NA NA NA Mean :161.3 Mean : 733.3 NA NA NA Mean : 2304 NA NA Mean :11.49 Mean :2.371 NA Mean : 3.494 NA Mean : 1.7676 NA NA Mean :11.49 Mean :2.436 Mean : 3.2321 NA Mean : 1.822 NA Mean :2.481 NA NA NA NA NA NA NA NA Mean : 4.4069 Mean : 4.4491 NA Mean : 5.744 Mean :-0.3631 Mean :0.20045
NA NA 3rd Qu.:2019 NA NA NA NA NA NA 3rd Qu.:21.31 NA 3rd Qu.:11.000 NA NA NA NA NA 3rd Qu.:175.0 3rd Qu.: 900.0 NA NA NA 3rd Qu.: 300 NA NA 3rd Qu.:10.00 3rd Qu.:5.000 NA 3rd Qu.: 1.083 NA 3rd Qu.: 0.1185 NA NA 3rd Qu.:10.00 3rd Qu.:5.000 3rd Qu.: 1.0505 NA 3rd Qu.: 0.130 NA 3rd Qu.:3.000 NA NA NA NA NA NA NA NA 3rd Qu.: 0.5600 3rd Qu.: 0.6516 NA 3rd Qu.: 5.000 3rd Qu.: 0.1849 3rd Qu.:0.28750
NA NA Max. :2020 NA NA NA NA NA NA Max. :79.11 NA Max. :14.000 NA NA NA NA NA Max. :300.0 Max. :1500.0 NA NA NA Max. :59777 NA NA Max. :60.00 Max. :6.000 NA Max. :86.689 NA Max. :133.7000 NA NA Max. :60.00 Max. :6.000 Max. :134.4379 NA Max. :130.500 NA Max. :4.000 NA NA NA NA NA NA NA NA Max. :133.7000 Max. :130.5000 NA Max. :30.000 Max. : 3.4622 Max. :1.43748
NA NA NA NA NA NA NA NA NA NA’s :284 NA NA NA NA NA NA NA NA’s :6 NA’s :56 NA NA NA NA’s :73 NA NA NA NA NA NA NA NA’s :53 NA NA NA NA NA NA NA’s :55 NA NA’s :198 NA NA NA NA NA NA NA NA NA’s :330 NA’s :328 NA NA NA NA

Intercept meta-analytical model

Determine the random effect structure

Cohort_ID explains virtually no variance in the model. Hence, it was removed from the model. All the other random effects explained significant variance and were kept in subsequent models

MA_all_rand_effects <- rma.mv(lnRR, VCV_lnRR, # Add `VCV_lnRR` to account for correlated errors errors between cohorts (shared_controls)
              random = list(~1|Study_ID, # Identity of the study
                            ~1|Phylogeny, # Phylogenetic correlation
                            ~1|Cohort_ID, # Identity of the cohort (shared controls)
                            ~1|Species_common, # Non-phylogenetic correlation between species
                            ~1|PFAS_type, # Type of PFAS 
                            ~1|Effect_ID), # Effect size identity 
              R= list(Phylogeny = cor_tree), # Assign the 'Phylogeny' argument to the phylogenetic variance-covariance matrix
              test = "t", 
              data = dat)

summary(MA_all_rand_effects) # Cohort ID does not explain any variance 
## 
## Multivariate Meta-Analysis Model (k = 512; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -634.9177  1269.8353  1283.8353  1313.4899  1284.0580   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.5844  0.7645     10     no        Study_ID   no 
## sigma^2.2  0.0092  0.0960     38     no       Phylogeny  yes 
## sigma^2.3  0.0000  0.0004    153     no       Cohort_ID   no 
## sigma^2.4  0.2081  0.4562     39     no  Species_common   no 
## sigma^2.5  0.1009  0.3177     18     no       PFAS_type   no 
## sigma^2.6  0.4877  0.6984    512     no       Effect_ID   no 
## 
## Test for Heterogeneity:
## Q(df = 511) = 7278.2801, p-val < .0001
## 
## Model Results:
## 
## estimate      se     tval    pval    ci.lb   ci.ub 
##  -0.3142  0.2917  -1.0770  0.2820  -0.8874  0.2589    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Intercept meta-analytical model and percentage of heterogeneity

MA_model <- rma.mv(lnRR, VCV_lnRR, 
              random = list(~1|Study_ID,
                            ~1|Phylogeny, # Removed Cohort_ID
                            ~1|Species_common, 
                            ~1|PFAS_type, 
                            ~1|Effect_ID), 
              R= list(Phylogeny = cor_tree), 
              test = "t", 
              data = dat)

summary(MA_model)
## 
## Multivariate Meta-Analysis Model (k = 512; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -634.9176  1269.8353  1281.8353  1307.2535  1282.0020   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.5844  0.7645     10     no        Study_ID   no 
## sigma^2.2  0.0092  0.0959     38     no       Phylogeny  yes 
## sigma^2.3  0.2081  0.4562     39     no  Species_common   no 
## sigma^2.4  0.1009  0.3177     18     no       PFAS_type   no 
## sigma^2.5  0.4877  0.6984    512     no       Effect_ID   no 
## 
## Test for Heterogeneity:
## Q(df = 511) = 7278.2801, p-val < .0001
## 
## Model Results:
## 
## estimate      se     tval    pval    ci.lb   ci.ub 
##  -0.3142  0.2917  -1.0771  0.2819  -0.8874  0.2589    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
i2_ml(MA_model) # Percentage of heterogeneity explained by each random effect
##          I2_total       I2_Study_ID      I2_Phylogeny I2_Species_common 
##       0.917318637       0.385600355       0.006068127       0.137303674 
##      I2_PFAS_type      I2_Effect_ID 
##       0.066572256       0.321774224
# plot
orchard_plot(MA_model, mod = "Int", xlab = "lnRR", alpha=0.4) +  # Orchard plot 
           geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5)+ # prediction intervals
           geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2)+ # confidence intervals
           geom_point(aes(fill = name),  size = 5, shape = 21)+ # mean estimate
           scale_colour_manual(values = "darkorange")+ # change colours
           scale_fill_manual(values="darkorange")+ 
           scale_size_continuous(range = c(1, 7))+ # change point scaling
           theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
                 text = element_text(size = 24), # change font sizes
                 legend.title = element_text(size = 15),
                 legend.text = element_text(size = 13)) 

save(MA_model, MA_all_rand_effects, file = here("Rdata", "int_MA_models.RData")) # save the models 

Meta-regressions

Function to run all models with the same structure

run_model<-function(data,formula){
  data<-as.data.frame(data) # convert data set into a data frame to calculate VCV matrix 
  VCV<-make_VCV_matrix(data
                       , V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
  
  rma.mv(lnRR, VCV, # run the model, as described earlier
         mods=formula,
         random = list(~1|Study_ID,
                       ~1|Phylogeny, 
                       ~1|Species_common, 
                       ~1|PFAS_type, 
                       ~1|Effect_ID), 
         R= list(Phylogeny = cor_tree), 
         test = "t", 
         data = data)
}

Function to run plots with the same structure

plot_continuous<-function(data, model, moderator, xlab){

pred<-predict.rma(model)

data %>% mutate(fit=pred$pred, 
               ci.lb=pred$ci.lb,
               ci.ub=pred$ci.ub,
               pr.lb=pred$cr.lb,
               pr.ub=pred$cr.ub) %>% 
ggplot(aes(x = moderator, y = lnRR)) +
     geom_ribbon(aes(ymin = pr.lb, ymax = pr.ub, color = NULL), alpha = .075) +
     geom_ribbon(aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = .2) +
     geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
     scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
     geom_line(aes(y = fit), size = 1.5)+  
  labs(x = xlab, y = "lnRR", size = "Precison (1/SE)") +
  theme_bw() +
  scale_size_continuous(range=c(1,9))+
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position=c(0,0), 
          legend.justification = c(0,0),
          legend.background = element_blank(), 
          legend.direction="horizontal",
          legend.title = element_text(size=15), 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))
}

Single-moderator models

All continuous variables were z-transformed

Cooking time

# Length_cooking_time_in_s

time_model<-run_model(dat, ~scale(Length_cooking_time_in_s)) # z-transformed
  
summary(time_model)
## 
## Multivariate Meta-Analysis Model (k = 456; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -525.7357  1051.4714  1065.4714  1094.2980  1065.7225   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.5387  0.7340      9     no        Study_ID   no 
## sigma^2.2  0.0000  0.0001     30     no       Phylogeny  yes 
## sigma^2.3  0.1425  0.3775     30     no  Species_common   no 
## sigma^2.4  0.1009  0.3176     17     no       PFAS_type   no 
## sigma^2.5  0.3964  0.6296    456     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 454) = 3999.2874, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 454) = 24.7942, p-val < .0001
## 
## Model Results:
## 
##                                  estimate      se     tval    pval    ci.lb 
## intrcpt                           -0.5531  0.2884  -1.9175  0.0558  -1.1199 
## scale(Length_cooking_time_in_s)   -0.2646  0.0531  -4.9794  <.0001  -0.3690 
##                                    ci.ub 
## intrcpt                           0.0138    . 
## scale(Length_cooking_time_in_s)  -0.1601  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(time_model)
##   R2_marginal R2_coditional 
##    0.05605974    0.68251140
# Plot
dat.time<-filter(dat, Length_cooking_time_in_s!="NA")
plot_continuous(dat.time, time_model, dat.time$Length_cooking_time_in_s, "Cooking time (s)")

Volume of liquid

# Volume_liquid_ml

volume_model<-run_model(dat, ~scale(log(Volume_liquid_ml))) # logged and z-transformed
  
summary(volume_model)
## 
## Multivariate Meta-Analysis Model (k = 439; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -552.0542  1104.1084  1118.1084  1146.6680  1118.3695   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.5079  0.7126      8     no        Study_ID   no 
## sigma^2.2  0.0048  0.0692     34     no       Phylogeny  yes 
## sigma^2.3  0.1498  0.3870     35     no  Species_common   no 
## sigma^2.4  0.1177  0.3431     18     no       PFAS_type   no 
## sigma^2.5  0.5153  0.7178    439     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 437) = 5805.2399, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 437) = 5.9117, p-val = 0.0154
## 
## Model Results:
## 
##                               estimate      se     tval    pval    ci.lb 
## intrcpt                        -0.3568  0.2978  -1.1981  0.2315  -0.9421 
## scale(log(Volume_liquid_ml))   -0.2543  0.1046  -2.4314  0.0154  -0.4599 
##                                 ci.ub 
## intrcpt                        0.2285    
## scale(log(Volume_liquid_ml))  -0.0487  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(volume_model)
##   R2_marginal R2_coditional 
##     0.0475590     0.6211531
# Plot
dat.volume<-filter(dat, Volume_liquid_ml!="NA")
plot_continuous(dat.volume, volume_model, log(dat.volume$Volume_liquid_ml), "Volume of liquid (mL)")

Cooking temperature

# Temperature_in_Celsius

temp_model <- run_model(dat, ~scale(Temperature_in_Celsius))  # z-transformed 

summary(temp_model)
## 
## Multivariate Meta-Analysis Model (k = 506; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -626.2464  1252.4927  1266.4927  1296.0508  1266.7185   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.5805  0.7619     10     no        Study_ID   no 
## sigma^2.2  0.0054  0.0735     38     no       Phylogeny  yes 
## sigma^2.3  0.2079  0.4559     39     no  Species_common   no 
## sigma^2.4  0.0974  0.3122     18     no       PFAS_type   no 
## sigma^2.5  0.4896  0.6997    506     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 504) = 7121.6638, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 504) = 0.0318, p-val = 0.8586
## 
## Model Results:
## 
##                                estimate      se     tval    pval    ci.lb 
## intrcpt                         -0.3001  0.2911  -1.0309  0.3031  -0.8719 
## scale(Temperature_in_Celsius)    0.0144  0.0810   0.1783  0.8586  -0.1447 
##                                 ci.ub 
## intrcpt                        0.2718    
## scale(Temperature_in_Celsius)  0.1736    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(temp_model)
##   R2_marginal R2_coditional 
##   0.000150956   0.645470163
# Plot
dat.temp <- filter(dat, Temperature_in_Celsius != "NA")
plot_continuous(dat.temp, temp_model, dat.temp$Temperature_in_Celsius, "Cooking temperature")

PFAS carbon chain length

# PFAS_carbon_chain

PFAS_model<-run_model(dat, ~PFAS_carbon_chain)
  
summary(PFAS_model)
## 
## Multivariate Meta-Analysis Model (k = 512; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -633.4258  1266.8515  1280.8515  1310.4924  1281.0746   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.5830  0.7636     10     no        Study_ID   no 
## sigma^2.2  0.0106  0.1028     38     no       Phylogeny  yes 
## sigma^2.3  0.2085  0.4566     39     no  Species_common   no 
## sigma^2.4  0.1061  0.3257     18     no       PFAS_type   no 
## sigma^2.5  0.4880  0.6985    512     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 510) = 7223.9798, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 510) = 0.1431, p-val = 0.7054
## 
## Model Results:
## 
##                    estimate      se     tval    pval    ci.lb   ci.ub 
## intrcpt             -0.4218  0.4087  -1.0322  0.3024  -1.2247  0.3810    
## PFAS_carbon_chain    0.0119  0.0315   0.3783  0.7054  -0.0499  0.0738    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(PFAS_model)
##   R2_marginal R2_coditional 
##  0.0005515213  0.6506803497
plot_continuous(dat, PFAS_model, dat$PFAS_carbon_chain, "PFAS carbon chain length")

Cooking category

# Cooking_Category

category_model<-run_model(dat, ~Cooking_Category-1)
  
summary(category_model)
## 
## Multivariate Meta-Analysis Model (k = 512; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -631.9971  1263.9942  1279.9942  1313.8537  1280.2822   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.5905  0.7685     10     no        Study_ID   no 
## sigma^2.2  0.0023  0.0481     38     no       Phylogeny  yes 
## sigma^2.3  0.2116  0.4600     39     no  Species_common   no 
## sigma^2.4  0.1023  0.3198     18     no       PFAS_type   no 
## sigma^2.5  0.4881  0.6986    512     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 509) = 7233.4707, p-val < .0001
## 
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 509) = 1.0533, p-val = 0.3686
## 
## Model Results:
## 
##                              estimate      se     tval    pval    ci.lb   ci.ub 
## Cooking_CategoryNo liquid     -0.2018  0.3125  -0.6457  0.5187  -0.8159  0.4122 
## Cooking_Categoryoil-based     -0.3712  0.2971  -1.2495  0.2121  -0.9548  0.2124 
## Cooking_Categorywater-based   -0.2932  0.2950  -0.9939  0.3207  -0.8729  0.2864 
##  
## Cooking_CategoryNo liquid 
## Cooking_Categoryoil-based 
## Cooking_Categorywater-based 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(category_model)
##   R2_marginal R2_coditional 
##   0.002563516   0.650990549
# plot
orchard_plot(category_model, mod = "Cooking_Category", xlab = "lnRR", alpha=0.4)+
           geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5)+ # prediction intervals
           geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0, show.legend = FALSE, size = 2)+ # confidence intervals
           geom_point(aes(fill = name),  size = 5, shape = 21)+ # mean estimate
           scale_colour_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3"))+ # change colours
           scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+ 
           scale_size_continuous(range = c(1, 7))+ # change point scaling
           theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
                 text = element_text(size = 24), # change font sizes
                 legend.title = element_text(size = 15),
                 legend.text = element_text(size = 13))

Percentage of moisture loss

This analysis is a posteriori and will only be presented in supplement.

# Moisture_loss_in_percent

moisture_model<-run_model(dat, ~scale(Moisture_loss_in_percent))
  
summary(moisture_model)
## 
## Multivariate Meta-Analysis Model (k = 228; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -234.1714   468.3428   482.3428   506.2865   482.8566   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.0775  0.2785      6     no        Study_ID   no 
## sigma^2.2  0.2316  0.4812     18     no       Phylogeny  yes 
## sigma^2.3  0.1307  0.3615     18     no  Species_common   no 
## sigma^2.4  0.0094  0.0968     17     no       PFAS_type   no 
## sigma^2.5  0.3220  0.5674    228     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 226) = 2492.6080, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 226) = 0.0295, p-val = 0.8638
## 
## Model Results:
## 
##                                  estimate      se     tval    pval    ci.lb 
## intrcpt                            0.5347  0.3311   1.6147  0.1078  -0.1178 
## scale(Moisture_loss_in_percent)   -0.0117  0.0683  -0.1717  0.8638  -0.1463 
##                                   ci.ub 
## intrcpt                          1.1872    
## scale(Moisture_loss_in_percent)  0.1229    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(moisture_model)
##   R2_marginal R2_coditional 
##  0.0001783538  0.5825498843
# Plot
dat.moisture<-filter(dat, Moisture_loss_in_percent!="NA")
plot_continuous(dat.moisture, moisture_model, dat.moisture$Moisture_loss_in_percent, "Percentage of moisture loss")

save(category_model, PFAS_model, temp_model, time_model, volume_model, moisture_model, 
    file = here("Rdata", "single_mod_models.RData"))  # Save models

Full model

# Full_model 

full_model <- run_model(dat, ~ - 1 + 
                               Cooking_Category +
                               scale(Temperature_in_Celsius) +
                               scale(Length_cooking_time_in_s) +
                               scale(PFAS_carbon_chain) +
                               scale(log(Volume_liquid_ml)))
summary(full_model)
## 
## Multivariate Meta-Analysis Model (k = 399; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -442.1936   884.3873   908.3873   956.0424   909.2105   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.0835  0.2890      7     no        Study_ID   no 
## sigma^2.2  0.0000  0.0001     26     no       Phylogeny  yes 
## sigma^2.3  0.0941  0.3067     26     no  Species_common   no 
## sigma^2.4  0.1212  0.3481     17     no       PFAS_type   no 
## sigma^2.5  0.3829  0.6188    399     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 392) = 2935.7864, p-val < .0001
## 
## Test of Moderators (coefficients 1:7):
## F(df1 = 7, df2 = 392) = 13.2854, p-val < .0001
## 
## Model Results:
## 
##                                  estimate      se     tval    pval    ci.lb 
## Cooking_CategoryNo liquid         -0.5772  0.2836  -2.0356  0.0425  -1.1348 
## Cooking_Categoryoil-based         -0.7378  0.1891  -3.9010  0.0001  -1.1097 
## Cooking_Categorywater-based       -0.4054  0.2202  -1.8409  0.0664  -0.8384 
## scale(Temperature_in_Celsius)     -0.0147  0.1119  -0.1317  0.8953  -0.2348 
## scale(Length_cooking_time_in_s)   -0.4005  0.0577  -6.9370  <.0001  -0.5140 
## scale(PFAS_carbon_chain)           0.0619  0.0799   0.7746  0.4390  -0.0952 
## scale(log(Volume_liquid_ml))      -0.7214  0.1027  -7.0271  <.0001  -0.9233 
##                                    ci.ub 
## Cooking_CategoryNo liquid        -0.0197    * 
## Cooking_Categoryoil-based        -0.3660  *** 
## Cooking_Categorywater-based       0.0276    . 
## scale(Temperature_in_Celsius)     0.2053      
## scale(Length_cooking_time_in_s)  -0.2870  *** 
## scale(PFAS_carbon_chain)          0.2189      
## scale(log(Volume_liquid_ml))     -0.5196  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(full_model)
##   R2_marginal R2_coditional 
##     0.4600194     0.6967281
full_modelb <- run_model(dat, ~ 1 +
                               relevel(factor(Cooking_Category),ref =  "oil-based") +
                               scale(Temperature_in_Celsius) +
                               scale(Length_cooking_time_in_s) +
                               scale(PFAS_carbon_chain) +
                               scale(log(Volume_liquid_ml)))
summary(full_modelb)
## 
## Multivariate Meta-Analysis Model (k = 399; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -442.1936   884.3873   908.3873   956.0424   909.2105   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.0835  0.2890      7     no        Study_ID   no 
## sigma^2.2  0.0000  0.0001     26     no       Phylogeny  yes 
## sigma^2.3  0.0941  0.3067     26     no  Species_common   no 
## sigma^2.4  0.1212  0.3481     17     no       PFAS_type   no 
## sigma^2.5  0.3829  0.6188    399     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 392) = 2935.7864, p-val < .0001
## 
## Test of Moderators (coefficients 2:7):
## F(df1 = 6, df2 = 392) = 12.9382, p-val < .0001
## 
## Model Results:
## 
##                                                                  estimate 
## intrcpt                                                           -0.7378 
## relevel(factor(Cooking_Category), ref = "oil-based")No liquid      0.1606 
## relevel(factor(Cooking_Category), ref = "oil-based")water-based    0.3324 
## scale(Temperature_in_Celsius)                                     -0.0147 
## scale(Length_cooking_time_in_s)                                   -0.4005 
## scale(PFAS_carbon_chain)                                           0.0619 
## scale(log(Volume_liquid_ml))                                      -0.7214 
##                                                                      se 
## intrcpt                                                          0.1891 
## relevel(factor(Cooking_Category), ref = "oil-based")No liquid    0.2243 
## relevel(factor(Cooking_Category), ref = "oil-based")water-based  0.1738 
## scale(Temperature_in_Celsius)                                    0.1119 
## scale(Length_cooking_time_in_s)                                  0.0577 
## scale(PFAS_carbon_chain)                                         0.0799 
## scale(log(Volume_liquid_ml))                                     0.1027 
##                                                                     tval 
## intrcpt                                                          -3.9010 
## relevel(factor(Cooking_Category), ref = "oil-based")No liquid     0.7159 
## relevel(factor(Cooking_Category), ref = "oil-based")water-based   1.9123 
## scale(Temperature_in_Celsius)                                    -0.1317 
## scale(Length_cooking_time_in_s)                                  -6.9370 
## scale(PFAS_carbon_chain)                                          0.7746 
## scale(log(Volume_liquid_ml))                                     -7.0271 
##                                                                    pval 
## intrcpt                                                          0.0001 
## relevel(factor(Cooking_Category), ref = "oil-based")No liquid    0.4745 
## relevel(factor(Cooking_Category), ref = "oil-based")water-based  0.0566 
## scale(Temperature_in_Celsius)                                    0.8953 
## scale(Length_cooking_time_in_s)                                  <.0001 
## scale(PFAS_carbon_chain)                                         0.4390 
## scale(log(Volume_liquid_ml))                                     <.0001 
##                                                                    ci.lb 
## intrcpt                                                          -1.1097 
## relevel(factor(Cooking_Category), ref = "oil-based")No liquid    -0.2805 
## relevel(factor(Cooking_Category), ref = "oil-based")water-based  -0.0093 
## scale(Temperature_in_Celsius)                                    -0.2348 
## scale(Length_cooking_time_in_s)                                  -0.5140 
## scale(PFAS_carbon_chain)                                         -0.0952 
## scale(log(Volume_liquid_ml))                                     -0.9233 
##                                                                    ci.ub 
## intrcpt                                                          -0.3660  *** 
## relevel(factor(Cooking_Category), ref = "oil-based")No liquid     0.6017      
## relevel(factor(Cooking_Category), ref = "oil-based")water-based   0.6742    . 
## scale(Temperature_in_Celsius)                                     0.2053      
## scale(Length_cooking_time_in_s)                                  -0.2870  *** 
## scale(PFAS_carbon_chain)                                          0.2189      
## scale(log(Volume_liquid_ml))                                     -0.5196  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
full_modelc <- run_model(dat, ~ 1 +
                               relevel(factor(Cooking_Category),ref =  "No liquid") +
                               scale(Temperature_in_Celsius) +
                               scale(Length_cooking_time_in_s) +
                               scale(PFAS_carbon_chain) +
                               scale(log(Volume_liquid_ml)))
summary(full_modelc)
## 
## Multivariate Meta-Analysis Model (k = 399; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -442.1936   884.3873   908.3873   956.0424   909.2105   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.0835  0.2890      7     no        Study_ID   no 
## sigma^2.2  0.0000  0.0001     26     no       Phylogeny  yes 
## sigma^2.3  0.0941  0.3067     26     no  Species_common   no 
## sigma^2.4  0.1212  0.3481     17     no       PFAS_type   no 
## sigma^2.5  0.3829  0.6188    399     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 392) = 2935.7864, p-val < .0001
## 
## Test of Moderators (coefficients 2:7):
## F(df1 = 6, df2 = 392) = 12.9382, p-val < .0001
## 
## Model Results:
## 
##                                                                  estimate 
## intrcpt                                                           -0.5772 
## relevel(factor(Cooking_Category), ref = "No liquid")oil-based     -0.1606 
## relevel(factor(Cooking_Category), ref = "No liquid")water-based    0.1718 
## scale(Temperature_in_Celsius)                                     -0.0147 
## scale(Length_cooking_time_in_s)                                   -0.4005 
## scale(PFAS_carbon_chain)                                           0.0619 
## scale(log(Volume_liquid_ml))                                      -0.7214 
##                                                                      se 
## intrcpt                                                          0.2836 
## relevel(factor(Cooking_Category), ref = "No liquid")oil-based    0.2243 
## relevel(factor(Cooking_Category), ref = "No liquid")water-based  0.2644 
## scale(Temperature_in_Celsius)                                    0.1119 
## scale(Length_cooking_time_in_s)                                  0.0577 
## scale(PFAS_carbon_chain)                                         0.0799 
## scale(log(Volume_liquid_ml))                                     0.1027 
##                                                                     tval 
## intrcpt                                                          -2.0356 
## relevel(factor(Cooking_Category), ref = "No liquid")oil-based    -0.7159 
## relevel(factor(Cooking_Category), ref = "No liquid")water-based   0.6499 
## scale(Temperature_in_Celsius)                                    -0.1317 
## scale(Length_cooking_time_in_s)                                  -6.9370 
## scale(PFAS_carbon_chain)                                          0.7746 
## scale(log(Volume_liquid_ml))                                     -7.0271 
##                                                                    pval 
## intrcpt                                                          0.0425 
## relevel(factor(Cooking_Category), ref = "No liquid")oil-based    0.4745 
## relevel(factor(Cooking_Category), ref = "No liquid")water-based  0.5161 
## scale(Temperature_in_Celsius)                                    0.8953 
## scale(Length_cooking_time_in_s)                                  <.0001 
## scale(PFAS_carbon_chain)                                         0.4390 
## scale(log(Volume_liquid_ml))                                     <.0001 
##                                                                    ci.lb 
## intrcpt                                                          -1.1348 
## relevel(factor(Cooking_Category), ref = "No liquid")oil-based    -0.6017 
## relevel(factor(Cooking_Category), ref = "No liquid")water-based  -0.3479 
## scale(Temperature_in_Celsius)                                    -0.2348 
## scale(Length_cooking_time_in_s)                                  -0.5140 
## scale(PFAS_carbon_chain)                                         -0.0952 
## scale(log(Volume_liquid_ml))                                     -0.9233 
##                                                                    ci.ub 
## intrcpt                                                          -0.0197    * 
## relevel(factor(Cooking_Category), ref = "No liquid")oil-based     0.2805      
## relevel(factor(Cooking_Category), ref = "No liquid")water-based   0.6916      
## scale(Temperature_in_Celsius)                                     0.2053      
## scale(Length_cooking_time_in_s)                                  -0.2870  *** 
## scale(PFAS_carbon_chain)                                          0.2189      
## scale(log(Volume_liquid_ml))                                     -0.5196  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# testing cooking categories  
full_modeld  <- rma.mv(yi = lnRR, V = VCV_lnRR, # run the model, as described earlier
                         mods= ~-1 +
                               Cooking_Category +
                               scale(Temperature_in_Celsius) +
                               scale(Length_cooking_time_in_s) +
                               scale(PFAS_carbon_chain) +
                               scale(log(Volume_liquid_ml)),
         random = list(~1|Study_ID,
                       ~1|Phylogeny, 
                       ~1|Species_common, 
                       ~1|PFAS_type, 
                       ~1|Effect_ID), 
         R= list(Phylogeny = cor_tree), 
         test = "t", 
         data = dat,
         btt = c(1:3)) # testing the significance of cooking category - testing first 3 regression coefficients) 

summary(full_modeld)
## 
## Multivariate Meta-Analysis Model (k = 399; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -442.1936   884.3873   908.3873   956.0424   909.2105   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.0835  0.2890      7     no        Study_ID   no 
## sigma^2.2  0.0000  0.0001     26     no       Phylogeny  yes 
## sigma^2.3  0.0941  0.3067     26     no  Species_common   no 
## sigma^2.4  0.1212  0.3481     17     no       PFAS_type   no 
## sigma^2.5  0.3829  0.6188    399     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 392) = 2935.7864, p-val < .0001
## 
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 392) = 5.3873, p-val = 0.0012
## 
## Model Results:
## 
##                                  estimate      se     tval    pval    ci.lb 
## Cooking_CategoryNo liquid         -0.5772  0.2836  -2.0356  0.0425  -1.1348 
## Cooking_Categoryoil-based         -0.7378  0.1891  -3.9010  0.0001  -1.1097 
## Cooking_Categorywater-based       -0.4054  0.2202  -1.8409  0.0664  -0.8384 
## scale(Temperature_in_Celsius)     -0.0147  0.1119  -0.1317  0.8953  -0.2348 
## scale(Length_cooking_time_in_s)   -0.4005  0.0577  -6.9370  <.0001  -0.5140 
## scale(PFAS_carbon_chain)           0.0619  0.0799   0.7746  0.4390  -0.0952 
## scale(log(Volume_liquid_ml))      -0.7214  0.1027  -7.0271  <.0001  -0.9233 
##                                    ci.ub 
## Cooking_CategoryNo liquid        -0.0197    * 
## Cooking_Categoryoil-based        -0.3660  *** 
## Cooking_Categorywater-based       0.0276    . 
## scale(Temperature_in_Celsius)     0.2053      
## scale(Length_cooking_time_in_s)  -0.2870  *** 
## scale(PFAS_carbon_chain)          0.2189      
## scale(log(Volume_liquid_ml))     -0.5196  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
save(full_model,full_modelb,full_modelc,full_modeld, file = here("Rdata", "full_model.RData"))

Check collinearity of predictors

## Check for collinerarity - seems fine

vif(full_model)
##       Cooking_CategoryNo liquid       Cooking_Categoryoil-based 
##                          1.6588                          3.3203 
##     Cooking_Categorywater-based   scale(Temperature_in_Celsius) 
##                          4.1165                          2.3290 
## scale(Length_cooking_time_in_s)        scale(PFAS_carbon_chain) 
##                          1.0993                          1.0018 
##    scale(log(Volume_liquid_ml)) 
##                          1.3527
dat %>% select(Temperature_in_Celsius, Length_cooking_time_in_s, PFAS_carbon_chain, 
    Volume_liquid_ml) %>% ggpairs()

Conditional analyses

Inspection of the plots highlighted potential significant decreases in PFAS content with increased cooking time and volume of cooking. Hence, here we used emmeans (download from remotes::install_github(“rvlenth/emmeans”, dependencies = TRUE, build_opts = "")) to generate marginalised means at specified values of the different predictors. Such analysis enable the quantification of the mean effect size after controlling for different values of the moderators.

Full model

# Full model in original units (not z-transformation)
dat$log_Volume_liquid_ml <- log(dat$Volume_liquid_ml)

full_model_org_units <- run_model(dat, ~-1 + Cooking_Category + Temperature_in_Celsius + 
    Length_cooking_time_in_s + PFAS_carbon_chain + log_Volume_liquid_ml)


save(full_model, file = here("Rdata", "full_model_org_units.RData"))

Overall marginalised mean

res <- marginal_means(full_model_org_units, data = dat, mod = "1")
res$mod_table
##      name   estimate   lowerCL    upperCL   lowerPR upperPR
## 1 Intrcpt -0.5555518 -0.940287 -0.1708165 -2.223783 1.11268

Marginal means for different cooking categories

res_cat <- marginal_means(full_model_org_units, data = dat, mod = "1", by = "Cooking_Category")
res_cat$mod_table
##      name   condition   estimate   lowerCL      upperCL   lowerPR   upperPR
## 1 Intrcpt   No liquid -0.5592896 -1.116595 -0.001984678 -2.275554 1.1569750
## 2 Intrcpt   oil-based -0.7198871 -1.091130 -0.348644669 -2.385059 0.9452845
## 3 Intrcpt water-based -0.3874786 -0.822767  0.047809860 -2.068089 1.2931319
orchard_plot(res_cat, xlab = "lnRR", condition.lab = "Cooking Category")

Marginal means for pre-determined cooking times

Here, we generate estimates at cooking times of 2, 10, and 25 min.

res_cooking_time <- marginal_means(full_model_org_units, data = dat, mod = "1", at = list(Length_cooking_time_in_s = c(120, 
    600, 1500)), by = "Length_cooking_time_in_s")
res_cooking_time$mod_table
##      name condition   estimate    lowerCL      upperCL   lowerPR    upperPR
## 1 Intrcpt       120  0.2760019 -0.1661358  0.718139542 -1.406396 1.95839931
## 2 Intrcpt       600 -0.3818220 -0.7675135  0.003869539 -2.050274 1.28663041
## 3 Intrcpt      1500 -1.6152417 -2.1137532 -1.116730238 -3.313326 0.08284225
orchard_plot(res_cooking_time, xlab = "lnRR", condition.lab = "Cooking time (sec)")

Marginalised means for each cooking category, at different cooking times

res_cooking_time_cat <- marginal_means(full_model_org_units, data = dat, mod = "Cooking_Category", 
    at = list(Length_cooking_time_in_s = c(120, 600, 1500)), by = "Length_cooking_time_in_s")
res_cooking_time_cat$mod_table
##          name condition   estimate     lowerCL    upperCL   lowerPR     upperPR
## 1   No liquid       120  0.2722640 -0.32805150  0.8725796 -1.458445  2.00297318
## 2   Oil-based       120  0.1116665 -0.31527016  0.5386032 -1.566800  1.79013319
## 3 Water-based       120  0.4440751 -0.04339344  0.9315436 -1.250800  2.13894999
## 4   No liquid       600 -0.3855598 -0.94396053  0.1728409 -2.102181  1.33106092
## 5   Oil-based       600 -0.5461573 -0.91754487 -0.1747698 -2.211361  1.11904661
## 6 Water-based       600 -0.2137488 -0.65004667  0.2225491 -1.894621  1.46712340
## 7   No liquid      1500 -1.6189795 -2.25782053 -0.9801386 -3.363426  0.12546684
## 8   Oil-based      1500 -1.7795770 -2.27166428 -1.2874898 -3.475786 -0.08336796
## 9 Water-based      1500 -1.4471685 -1.98484449 -0.9094925 -3.157160  0.26282299
orchard_plot(res_cooking_time_cat, xlab = "lnRR", condition.lab = "Cooking time (sec)")

Marginal means for different volumes of liquid

Here, we generate marginalised estimates at volumes of liquid of ~10, 500, and 10000 mL. We did not look at the means for different cooking categories because they are inherently different in the volume of liquid used.

res_volume <- marginal_means(full_model_org_units, data = dat, mod = "1", at = list(log_Volume_liquid_ml = c(2.3, 
    5.5, 9.2)), by = "log_Volume_liquid_ml")
res_volume$mod_table
##      name condition   estimate    lowerCL    upperCL   lowerPR    upperPR
## 1 Intrcpt       2.3  0.3127963 -0.1724533  0.7980458 -1.381442  2.0070343
## 2 Intrcpt       5.5 -0.7911205 -1.1714546 -0.4107863 -2.458343  0.8761016
## 3 Intrcpt       9.2 -2.0675242 -2.5942812 -1.5407672 -3.774114 -0.3609345
orchard_plot(res_volume, xlab = "lnRR", condition.lab = "ln(liquid volume (ml))")

Marginal means for different PFAS carbon chains

Here, we generate marginalized estimates for PFAS of 3, 6, and 12 carbon chains

res_PFAS <- marginal_means(full_model_org_units, data = dat, mod = "1", at = list(PFAS_carbon_chain = c(3, 
    6, 12)), by = "PFAS_carbon_chain")
res_PFAS$mod_table
##      name condition   estimate   lowerCL     upperCL   lowerPR  upperPR
## 1 Intrcpt         3 -0.7154681 -1.282498 -0.14843860 -2.434915 1.003979
## 2 Intrcpt         6 -0.6358094 -1.076165 -0.19545424 -2.317739 1.046120
## 3 Intrcpt        12 -0.4764920 -0.905390 -0.04759398 -2.155459 1.202475
orchard_plot(res_PFAS, xlab = "lnRR", condition.lab = "PFAS carbon chain")

Marginalised mean estimate for each PFAS carbon chain, for each cooking category

res_PFAS_cat <- marginal_means(full_model_org_units, data = dat, mod = "Cooking_Category", 
    at = list(PFAS_carbon_chain = c(3, 6, 12)), by = "PFAS_carbon_chain")
res_PFAS_cat$mod_table
##          name condition   estimate    lowerCL     upperCL   lowerPR   upperPR
## 1   No liquid         3 -0.7192060 -1.4144067 -0.02400524 -2.485071 1.0466591
## 2   Oil-based         3 -0.8798035 -1.4369620 -0.32264494 -2.596021 0.8364136
## 3 Water-based         3 -0.5473949 -1.1512657  0.05647581 -2.279340 1.1845506
## 4   No liquid         6 -0.6395473 -1.2362765 -0.04281803 -2.369016 1.0899212
## 5   Oil-based         6 -0.8001448 -1.2282364 -0.37205314 -2.478906 0.8786160
## 6 Water-based         6 -0.4677362 -0.9537526  0.01828016 -2.162194 1.2267216
## 7   No liquid        12 -0.4802298 -1.0692202  0.10876054 -2.207043 1.2465837
## 8   Oil-based        12 -0.6408273 -1.0581959 -0.22345881 -2.316886 1.0352311
## 9 Water-based        12 -0.3084188 -0.7823138  0.16547620 -1.999440 1.3826022
orchard_plot(res_PFAS_cat, xlab = "lnRR", condition.lab = "PFAS carbon chain")

##

Sub-group analyses for each cooking category

Here, we investigated whether the effect of the continuous moderators on lnRR vary depending on the cooking category. Hence, we performed subset analyses for each cooking category.

Oil-based cooking

Subset data and update function

oil_dat<-filter(dat, Cooking_Category=="oil-based")

include <- row.names(cor_tree) %in% oil_dat$Phylogeny # Check which rows are present in the phylogenetic tree 
cor_tree_oil <- cor_tree[include, include] # Only include the species that match the reduced data set 


run_model_oil<-function(data,formula){
  data<-as.data.frame(data) # convert data set into a data frame to calculate VCV matrix 
  VCV<-make_VCV_matrix(data, V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
  
  rma.mv(lnRR, VCV, # run the model, as described earlier
         mods=formula,
         random = list(~1|Study_ID,
                       ~1|Phylogeny, 
                       ~1|Species_common, 
                       ~1|PFAS_type, 
                       ~1|Effect_ID), 
         R= list(Phylogeny = cor_tree_oil), # cor_tree_oil here
         test = "t", 
         data = data)
}

Full model

full_model_oil <- run_model_oil(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) + 
    scale(PFAS_carbon_chain) + scale(log(Volume_liquid_ml)))

summary(full_model_oil)
## 
## Multivariate Meta-Analysis Model (k = 263; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -184.0059   368.0118   388.0118   423.5414   388.9025   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.0000  0.0001      6     no        Study_ID   no 
## sigma^2.2  0.0000  0.0000     19     no       Phylogeny  yes 
## sigma^2.3  0.0141  0.1188     19     no  Species_common   no 
## sigma^2.4  0.0433  0.2080     16     no       PFAS_type   no 
## sigma^2.5  0.1124  0.3353    263     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 258) = 573.2766, p-val < .0001
## 
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 258) = 27.1829, p-val < .0001
## 
## Model Results:
## 
##                                  estimate      se     tval    pval    ci.lb 
## intrcpt                           -0.4370  0.0959  -4.5554  <.0001  -0.6259 
## scale(Temperature_in_Celsius)     -0.0039  0.0817  -0.0474  0.9622  -0.1647 
## scale(Length_cooking_time_in_s)   -0.3805  0.0485  -7.8388  <.0001  -0.4761 
## scale(PFAS_carbon_chain)           0.1286  0.0613   2.0957  0.0371   0.0078 
## scale(log(Volume_liquid_ml))      -0.4032  0.0893  -4.5131  <.0001  -0.5791 
##                                    ci.ub 
## intrcpt                          -0.2481  *** 
## scale(Temperature_in_Celsius)     0.1570      
## scale(Length_cooking_time_in_s)  -0.2849  *** 
## scale(PFAS_carbon_chain)          0.2494    * 
## scale(log(Volume_liquid_ml))     -0.2273  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
save(full_model_oil, file = here("Rdata", "full_model_oil.RData"))

Water-based cooking

Subset data and updating functions

water_dat<-filter(dat, Cooking_Category=="water-based")

include <- row.names(cor_tree) %in% water_dat$Phylogeny # Check which rows are present in the phylogenetic tree 
cor_tree_water <- cor_tree[include, include] # Only include the species that match the reduced data set 


run_model_water<-function(data,formula){
  data<-as.data.frame(data) # convert data set into a data frame to calculate VCV matrix 
  VCV<-make_VCV_matrix(data, V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
  
  rma.mv(lnRR, VCV, # run the model, as described earlier
         mods=formula,
         random = list(~1|Study_ID,
                       ~1|Phylogeny, 
                       ~1|Species_common, 
                       ~1|PFAS_type, 
                       ~1|Effect_ID), 
         R= list(Phylogeny = cor_tree_water), # cor_tree_water here
         test = "t", 
         data = data)
}

Full model

full_model_water <- run_model_water(water_dat, ~
                                           scale(Length_cooking_time_in_s) +
                                           scale(PFAS_carbon_chain) +
                                           scale(log(Volume_liquid_ml)))
                 
summary(full_model_water)
## 
## Multivariate Meta-Analysis Model (k = 121; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -179.5070   359.0139   377.0139   401.8735   378.6961   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.3578  0.5982      6     no        Study_ID   no 
## sigma^2.2  0.0000  0.0002     19     no       Phylogeny  yes 
## sigma^2.3  0.0000  0.0039     19     no  Species_common   no 
## sigma^2.4  0.4043  0.6359     15     no       PFAS_type   no 
## sigma^2.5  0.9470  0.9732    121     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 117) = 2237.4353, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 117) = 4.6171, p-val = 0.0043
## 
## Model Results:
## 
##                                  estimate      se     tval    pval    ci.lb 
## intrcpt                           -0.9408  0.3396  -2.7700  0.0065  -1.6134 
## scale(Length_cooking_time_in_s)   -0.4503  0.1591  -2.8307  0.0055  -0.7653 
## scale(PFAS_carbon_chain)          -0.0082  0.1688  -0.0487  0.9612  -0.3426 
## scale(log(Volume_liquid_ml))      -0.7986  0.2779  -2.8732  0.0048  -1.3491 
##                                    ci.ub 
## intrcpt                          -0.2682  ** 
## scale(Length_cooking_time_in_s)  -0.1353  ** 
## scale(PFAS_carbon_chain)          0.3261     
## scale(log(Volume_liquid_ml))     -0.2481  ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
save(full_model_water, file = here("Rdata", "full_model_water.RData"))

Dry cooking

Not very relevant because all effect sizes are from one study here. Also, the model does not converge when adding VCV_lnRR

dry_dat<-filter(dat, Cooking_Category=="No liquid")

include <- row.names(cor_tree) %in% dry_dat$Phylogeny # Check which rows are present in the phylogenetic tree 
cor_tree_dry <- cor_tree[include, include] # Only include the species that match the reduced data set 


run_model_dry<-function(data,formula){
  data<-as.data.frame(data) # convert data set into a data frame to calculate VCV matrix 
  rma.mv(lnRR, var_lnRR, # run the model with var_lnRR instead of VCV
         mods=formula,
         random = list(~1|Study_ID,
                       ~1|Phylogeny, 
                       ~1|Species_common, 
                       ~1|PFAS_type, 
                       ~1|Effect_ID), 
         R= list(Phylogeny = cor_tree_dry), # cor_tree_dry here
         test = "t", 
         data = data)
}

Full model

full_model_dry <- run_model_dry(dry_dat, ~ scale(Length_cooking_time_in_s)) # Model does not converge with VCV_lnRR
                 
summary(full_model_dry)
## 
## Multivariate Meta-Analysis Model (k = 47; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -12.9722   25.9445   37.9445   48.7844   40.1550   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.0000  0.0000      1    yes        Study_ID   no 
## sigma^2.2  0.0046  0.0679      8     no       Phylogeny  yes 
## sigma^2.3  0.0022  0.0471      8     no  Species_common   no 
## sigma^2.4  0.0735  0.2711      2     no       PFAS_type   no 
## sigma^2.5  0.0000  0.0000     47     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 45) = 40.1184, p-val = 0.6785
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 45) = 38.2787, p-val < .0001
## 
## Model Results:
## 
##                                  estimate      se     tval    pval    ci.lb 
## intrcpt                           -0.7770  0.2071  -3.7513  0.0005  -1.1942 
## scale(Length_cooking_time_in_s)   -0.3461  0.0559  -6.1870  <.0001  -0.4588 
##                                    ci.ub 
## intrcpt                          -0.3598  *** 
## scale(Length_cooking_time_in_s)  -0.2334  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
save(full_model_dry, file = here("Rdata", "full_model_dry.RData"))

Plots

Cooking time

Generate predictions

  oil_dat <- filter(dat, Cooking_Category=="oil-based")
  water_dat <- filter(dat, Cooking_Category=="water-based")
  dry_dat <- filter(dat, Cooking_Category=="No liquid")

  oil_dat_time<-filter(oil_dat, Length_cooking_time_in_s!="NA") 
  water_dat_time<-filter(water_dat, Length_cooking_time_in_s!="NA") 
  dry_dat_time<-filter(dry_dat, Length_cooking_time_in_s!="NA")
  
model_oil_time<-run_model_oil(oil_dat_time, ~Length_cooking_time_in_s) 
model_water_time<-run_model_water(water_dat_time, ~Length_cooking_time_in_s) 
model_dry_time<-run_model_dry(dry_dat_time, ~Length_cooking_time_in_s) 


pred_oil_time<-predict.rma(model_oil_time)
pred_water_time<-predict.rma(model_water_time)
pred_dry_time<-predict.rma(model_dry_time)

oil_dat_time<-mutate(oil_dat_time,
                    ci.lb = pred_oil_time$ci.lb, # lower bound of the confidence interval for oil
                    ci.ub = pred_oil_time$ci.ub, # upper bound of the confidence interval for oil
                    fit = pred_oil_time$pred) # regression line for oil

water_dat_time<-mutate(water_dat_time,
                    ci.lb = pred_water_time$ci.lb, # lower bound of the confidence interval for water
                    ci.ub = pred_water_time$ci.ub, # upper bound of the confidence interval for water
                    fit = pred_water_time$pred) # regression line for water

dry_dat_time<-mutate(dry_dat_time,
                    ci.lb = pred_dry_time$ci.lb, # lower bound of the confidence interval for dry
                    ci.ub = pred_dry_time$ci.ub, # upper bound of the confidence interval for dry
                    fit = pred_dry_time$pred) # regression line for dry

Plot

ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
  
       geom_ribbon(data=water_dat_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=water_dat_time,aes(y = fit), size = 1.5, col="dodgerblue")+  
  
       geom_ribbon(data=oil_dat_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
       geom_line(data=oil_dat_time,aes(y = fit), size = 1.5, col="goldenrod")+  
  
         geom_ribbon(data=dry_dat_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=dry_dat_time,aes(y = fit), size = 1.5, col="palegreen3")+  
  
  
       geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
       scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
       labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position=c(0,0), 
          legend.justification = c(0,0),
          legend.background = element_blank(), 
          legend.direction="horizontal",
          legend.title = element_text(size=15), 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Predictions with the full model

##### Oil based
full_model_oil_time<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
                                           Length_cooking_time_in_s+
                                           scale(PFAS_carbon_chain) +
                                           scale(log(Volume_liquid_ml)))

pred_oil_time<-predict.rma(full_model_oil_time, addx=TRUE, newmods=cbind(0,c(120:1500), 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time<-as.data.frame(pred_oil_time)
pred_oil_time$Length_cooking_time_in_s=pred_oil_time$X.Length_cooking_time_in_s
pred_oil_time<-left_join(oil_dat, pred_oil_time, by="Length_cooking_time_in_s")


##### Water based
full_model_water_time<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
                                           Length_cooking_time_in_s+
                                           scale(PFAS_carbon_chain) +
                                           scale(log(Volume_liquid_ml)))

pred_water_time<-predict.rma(full_model_water_time, addx=TRUE, newmods=cbind(c(120:1500), 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time<-as.data.frame(pred_water_time)
pred_water_time$Length_cooking_time_in_s=pred_water_time$X.Length_cooking_time_in_s
pred_water_time<-left_join(water_dat, pred_water_time, by="Length_cooking_time_in_s")

##### No liquid 

full_model_dry_time<- run_model_dry(dry_dat, ~ Length_cooking_time_in_s)

pred_dry_time<-predict.rma(full_model_dry_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time<-as.data.frame(pred_dry_time)
pred_dry_time$Length_cooking_time_in_s=pred_dry_time$X.Length_cooking_time_in_s
pred_dry_time<-left_join(dry_dat, pred_dry_time, by="Length_cooking_time_in_s")




ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
  
       geom_ribbon(data=pred_water_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_water_time,aes(y = pred), size = 1.5, col="dodgerblue")+  
  
       geom_ribbon(data=pred_oil_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_oil_time,aes(y = pred), size = 1.5, col="goldenrod")+  
  
        geom_ribbon(data=pred_dry_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_dry_time,aes(y = pred), size = 1.5, col="palegreen3")+  
  
  
       geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
       scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
       labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position=c(0,0), 
          legend.justification = c(0,0),
          legend.background = element_blank(), 
          legend.direction="horizontal",
          legend.title = element_text(size=15), 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Volume of liquid

Generate predictions

oil_dat_vol <- filter(oil_dat, Volume_liquid_ml != "NA")
water_dat_vol <- filter(water_dat, Volume_liquid_ml != "NA")

model_oil_vol <- run_model_oil(oil_dat_vol, ~log(Volume_liquid_ml))
model_water_vol <- run_model_water(water_dat_vol, ~log(Volume_liquid_ml))


pred_oil_vol <- predict.rma(model_oil_vol)
pred_water_vol <- predict.rma(model_water_vol)

oil_dat_vol <- mutate(oil_dat_vol, ci.lb = pred_oil_vol$ci.lb, ci.ub = pred_oil_vol$ci.ub, 
    fit = pred_oil_vol$pred)

water_dat_vol <- mutate(water_dat_vol, ci.lb = pred_water_vol$ci.lb, ci.ub = pred_water_vol$ci.ub, 
    fit = pred_water_vol$pred)

oil_dat$log_Volume_liquid_ml <- log(oil_dat$Volume_liquid_ml)
water_dat$log_Volume_liquid_ml <- log(water_dat$Volume_liquid_ml)

Plot

ggplot(dat, aes(x = log(Volume_liquid_ml), y = lnRR, fill = Cooking_Category)) + 
    
geom_ribbon(data = water_dat_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), 
    alpha = 0.2) + geom_line(data = water_dat_vol, aes(y = fit), size = 1.5, col = "dodgerblue") + 
    
geom_ribbon(data = oil_dat_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) + 
    geom_line(data = oil_dat_vol, aes(y = fit), size = 1.5, col = "goldenrod") + 
    
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) + 
    scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Volume of liquid (mL))", 
    y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) + 
    theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) + 
    theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14), 
        legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(), 
        legend.direction = "horizontal", legend.title = element_text(size = 15), 
        panel.border = element_rect(colour = "black", fill = NA, size = 1.2))

Predictions with the full model

##### Oil based
full_model_oil_vol <- run_model_oil(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) + 
    scale(PFAS_carbon_chain) + log_Volume_liquid_ml)
pred_oil_vol <- predict.rma(full_model_oil_vol, addx = TRUE, newmods = cbind(0, 0, 
    0, c(log(5):log(750))))  # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time

pred_oil_vol <- as.data.frame(pred_oil_vol)
pred_oil_vol <- pred_oil_vol %>% mutate(Volume_liquid_ml = exp(X.log_Volume_liquid_ml), 
    Cooking_Category = "oil-based", lnRR = 0)  # for the plot to work, we need to add a column with cooking category and a column with lnRR


##### Water based

full_model_water_vol <- run_model_water(water_dat, ~scale(Temperature_in_Celsius) + 
    scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + log_Volume_liquid_ml)

pred_water_vol <- predict.rma(full_model_water_vol, addx = TRUE, newmods = cbind(0, 
    0, c(log(250):log(59777))))  # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time

pred_water_vol <- as.data.frame(pred_water_vol)
pred_water_vol <- pred_water_vol %>% mutate(Volume_liquid_ml = exp(X.log_Volume_liquid_ml), 
    Cooking_Category = "water-based", lnRR = 0)



ggplot(dat, aes(x = log(Volume_liquid_ml), y = lnRR, fill = Cooking_Category)) + 
    
geom_ribbon(data = pred_water_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), 
    alpha = 0.2) + geom_line(data = pred_water_vol, aes(y = pred), size = 1.5, col = "dodgerblue") + 
    
geom_ribbon(data = pred_oil_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) + 
    geom_line(data = pred_oil_vol, aes(y = pred), size = 1.5, col = "goldenrod") + 
    

geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) + 
    scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Volume of liquid (mL))", 
    y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) + 
    theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) + 
    theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14), 
        legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(), 
        legend.direction = "horizontal", legend.title = element_text(size = 15), 
        panel.border = element_rect(colour = "black", fill = NA, size = 1.2))  #### The line doesn't go all the way down for water-based because the highest values are not included in the full model

PFAS carbon chain length

Generate predictions

oil_dat_PFAS <- filter(oil_dat, PFAS_carbon_chain != "NA")
water_dat_PFAS <- filter(water_dat, PFAS_carbon_chain != "NA")
dry_dat_PFAS <- filter(dry_dat, PFAS_carbon_chain != "NA")

model_oil_PFAS <- run_model_oil(oil_dat_PFAS, ~PFAS_carbon_chain)
model_water_PFAS <- run_model_water(water_dat_PFAS, ~PFAS_carbon_chain)
model_dry_PFAS <- run_model_dry(dry_dat_PFAS, ~PFAS_carbon_chain)


pred_oil_PFAS <- predict.rma(model_oil_PFAS)
pred_water_PFAS <- predict.rma(model_water_PFAS)
pred_dry_PFAS <- predict.rma(model_dry_PFAS)

oil_dat_PFAS <- mutate(oil_dat_PFAS, ci.lb = pred_oil_PFAS$ci.lb, ci.ub = pred_oil_PFAS$ci.ub, 
    fit = pred_oil_PFAS$pred)

water_dat_PFAS <- mutate(water_dat_PFAS, ci.lb = pred_water_PFAS$ci.lb, ci.ub = pred_water_PFAS$ci.ub, 
    fit = pred_water_PFAS$pred)

dry_dat_PFAS <- mutate(dry_dat_PFAS, ci.lb = pred_dry_PFAS$ci.lb, ci.ub = pred_dry_PFAS$ci.ub, 
    fit = pred_dry_PFAS$pred)

Plot

ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + 
geom_ribbon(data = dry_dat_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) + 
    geom_line(data = dry_dat_PFAS, aes(y = fit), size = 1.5, col = "palegreen3") + 
    
geom_ribbon(data = water_dat_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), 
    alpha = 0.2) + geom_line(data = water_dat_PFAS, aes(y = fit), size = 1.5, col = "dodgerblue") + 
    
geom_ribbon(data = oil_dat_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) + 
    geom_line(data = oil_dat_PFAS, aes(y = fit), size = 1.5, col = "goldenrod") + 
    


geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) + 
    scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "PFAS carbon chain length", 
    y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) + 
    theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) + 
    theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14), 
        legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(), 
        legend.direction = "horizontal", legend.title = element_text(size = 15), 
        panel.border = element_rect(colour = "black", fill = NA, size = 1.2))

Predictions with the full model

##### Oil based
full_model_oil_PFAS<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
                                           scale(Length_cooking_time_in_s)+
                                           PFAS_carbon_chain +
                                           scale(log(Volume_liquid_ml)))
pred_oil_PFAS<-predict.rma(full_model_oil_PFAS, addx=TRUE, newmods=cbind(0,0, c(3:14),0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS<-as.data.frame(pred_oil_PFAS)
pred_oil_PFAS$PFAS_carbon_chain=pred_oil_PFAS$X.PFAS_carbon_chain
pred_oil_PFAS<-left_join(oil_dat, pred_oil_PFAS, by="PFAS_carbon_chain")


##### Water based
full_model_water_PFAS<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
                                           scale(Length_cooking_time_in_s)+
                                           PFAS_carbon_chain +
                                           scale(log(Volume_liquid_ml)))

pred_water_PFAS<-predict.rma(full_model_water_PFAS, addx=TRUE, newmods=cbind(0, c(3:14),0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS<-as.data.frame(pred_water_PFAS)
pred_water_PFAS$PFAS_carbon_chain=pred_water_PFAS$X.PFAS_carbon_chain
pred_water_PFAS<-left_join(water_dat, pred_water_PFAS, by="PFAS_carbon_chain")

##### No liquid 

full_model_dry_PFAS<- run_model_dry(dry_dat, ~ PFAS_carbon_chain)

pred_dry_PFAS<-predict.rma(full_model_dry_PFAS, addx=TRUE)
pred_dry_PFAS<-as.data.frame(pred_dry_PFAS)
pred_dry_PFAS$PFAS_carbon_chain=pred_dry_PFAS$X.PFAS_carbon_chain
pred_dry_PFAS<-left_join(dry_dat, pred_dry_PFAS, by="PFAS_carbon_chain")




ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
  
    
       geom_ribbon(data=pred_dry_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_dry_PFAS,aes(y = pred), size = 1.5, col="palegreen3")+  
  
  
       geom_ribbon(data=pred_water_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_water_PFAS,aes(y = pred), size = 1.5, col="dodgerblue")+  
  
  
       geom_ribbon(data=pred_oil_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
       geom_line(data=pred_oil_PFAS,aes(y = pred), size = 1.5, col="goldenrod")+  
  
  
       geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
       scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
       labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position=c(0,0), 
          legend.justification = c(0,0),
          legend.background = element_blank(), 
          legend.direction="horizontal",
          legend.title = element_text(size=15), 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Publication bias

Funnel plot

funnel(full_model, yaxis = "seinv")

funnel(full_model)

Egger regressions

egger_all <- run_model(dat, ~ - 1 + Cooking_Category +
                      I(sqrt(1/N_tilde)) +  
                      scale(Publication_year) + 
                      scale(Temperature_in_Celsius) +
                      scale(Length_cooking_time_in_s) +
                      scale(PFAS_carbon_chain) +
                      scale(log(Volume_liquid_ml)))
summary(egger_all)
## 
## Multivariate Meta-Analysis Model (k = 399; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -437.7512   875.5024   903.5024   959.0284   904.6224   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.1374  0.3707      7     no        Study_ID   no 
## sigma^2.2  0.0000  0.0001     26     no       Phylogeny  yes 
## sigma^2.3  0.0082  0.0907     26     no  Species_common   no 
## sigma^2.4  0.1188  0.3447     17     no       PFAS_type   no 
## sigma^2.5  0.3978  0.6307    399     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 390) = 2866.3281, p-val < .0001
## 
## Test of Moderators (coefficients 1:9):
## F(df1 = 9, df2 = 390) = 9.9236, p-val < .0001
## 
## Model Results:
## 
##                                  estimate      se     tval    pval    ci.lb 
## Cooking_CategoryNo liquid         -0.2088  0.3994  -0.5227  0.6015  -0.9940 
## Cooking_Categoryoil-based         -0.3346  0.3457  -0.9679  0.3337  -1.0141 
## Cooking_Categorywater-based        0.0285  0.3478   0.0821  0.9346  -0.6553 
## I(sqrt(1/N_tilde))                -0.7252  0.5313  -1.3649  0.1731  -1.7699 
## scale(Publication_year)            0.1942  0.1222   1.5890  0.1129  -0.0461 
## scale(Temperature_in_Celsius)      0.0243  0.1107   0.2195  0.8263  -0.1934 
## scale(Length_cooking_time_in_s)   -0.3939  0.0583  -6.7509  <.0001  -0.5086 
## scale(PFAS_carbon_chain)           0.0708  0.0800   0.8849  0.3767  -0.0865 
## scale(log(Volume_liquid_ml))      -0.6800  0.1037  -6.5596  <.0001  -0.8839 
##                                    ci.ub 
## Cooking_CategoryNo liquid         0.5765      
## Cooking_Categoryoil-based         0.3450      
## Cooking_Categorywater-based       0.7124      
## I(sqrt(1/N_tilde))                0.3194      
## scale(Publication_year)           0.4346      
## scale(Temperature_in_Celsius)     0.2420      
## scale(Length_cooking_time_in_s)  -0.2792  *** 
## scale(PFAS_carbon_chain)          0.2282      
## scale(log(Volume_liquid_ml))     -0.4762  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
funnel(egger_all, yaxis = "seinv")

funnel(egger_all)

#funnel(egger_all, yaxis = "seinv")
# little evidence
egger_n <- run_model(dat, ~ I(sqrt(1/N_tilde)))
summary(egger_n)
## 
## Multivariate Meta-Analysis Model (k = 512; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -632.8391  1265.6782  1279.6782  1309.3191  1279.9013   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.6010  0.7752     10     no        Study_ID   no 
## sigma^2.2  0.0044  0.0664     38     no       Phylogeny  yes 
## sigma^2.3  0.1987  0.4458     39     no  Species_common   no 
## sigma^2.4  0.1008  0.3175     18     no       PFAS_type   no 
## sigma^2.5  0.4887  0.6991    512     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 510) = 6790.0424, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 510) = 0.6334, p-val = 0.4265
## 
## Model Results:
## 
##                     estimate      se     tval    pval    ci.lb   ci.ub 
## intrcpt              -0.0930  0.4055  -0.2294  0.8186  -0.8896  0.7036    
## I(sqrt(1/N_tilde))   -0.5005  0.6289  -0.7959  0.4265  -1.7361  0.7350    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
save(egger_all, egger_n, file = here("Rdata", "egger_regressions.RData"))

Publication year

pub_year<-run_model(dat, ~Publication_year)
summary(pub_year)
## 
## Multivariate Meta-Analysis Model (k = 512; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -631.7358  1263.4716  1277.4716  1307.1125  1277.6947   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.5567  0.7461     10     no        Study_ID   no 
## sigma^2.2  0.0145  0.1206     38     no       Phylogeny  yes 
## sigma^2.3  0.2046  0.4524     39     no  Species_common   no 
## sigma^2.4  0.1014  0.3184     18     no       PFAS_type   no 
## sigma^2.5  0.4878  0.6984    512     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 510) = 7278.1828, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 510) = 1.2853, p-val = 0.2574
## 
## Model Results:
## 
##                    estimate        se     tval    pval      ci.lb     ci.ub 
## intrcpt           -165.8555  146.0186  -1.1359  0.2566  -452.7275  121.0165    
## Publication_year     0.0821    0.0724   1.1337  0.2574    -0.0602    0.2243    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot_continuous(dat, pub_year, dat$Publication_year, "Publication year")

##

Sensitivity analyses

Leave-one-out analyses

Here, we iteratively removed one study at the time and investigated how it affects the overall mean. Removing one of the study particularly modifies the estimate, but none of these models show a significant overall difference in PFAS concentration with cooking.

dat$Study_ID<-as.factor(dat$Study_ID)
dat<-as.data.frame(dat) # Only work with a dataframe
VCV_matrix<-list() # will need new VCV matrices because the sample size will be iteratively reduced
Leave1studyout<-list() # create a list that will host the results of each model 
for(i in 1:length(levels(dat$Study_ID))){ # N models = N studies 
  VCV_matrix[[i]]<-make_VCV_matrix(dat[dat$Study_ID != levels(dat$Study_ID)[i], ], V="var_lnRR", cluster="Cohort_ID", obs="Effect_ID") # Create a new VCV matrix for each new model
  Leave1studyout[[i]] <- rma.mv(yi = lnRR, V = VCV_matrix[[i]], # Same model structure as all the models we fitted
                                random = list(~1|Study_ID,
                                              ~1|Phylogeny, 
                                              ~1|Species_common, 
                                              ~1|PFAS_type, 
                                              ~1|Effect_ID),
                                R= list(Phylogeny = cor_tree), 
                                test = "t", 
                                data = dat[dat$Study_ID != levels(dat$Study_ID)[i], ]) # Generate a new model for each new data (iterative removal of one study at a time)
}

# The output is a list so we need to summarise the coefficients of all the models performed

results.Leave1studyout<-as.data.frame(cbind(
                                           sapply(Leave1studyout, function(x) summary(x)$beta), # extract the beta coefficient from all models
                                           sapply(Leave1studyout, function(x) summary(x)$se), # extract the standard error from all models
                                           sapply(Leave1studyout, function(x) summary(x)$zval),  # extract the z value from all models
                                           sapply(Leave1studyout, function(x) summary(x)$pval), # extract the p value from all models
                                           sapply(Leave1studyout, function(x) summary(x)$ci.lb), # extract the lower confidence interval for all models
                                           sapply(Leave1studyout, function(x) summary(x)$ci.ub))) # extract the upper confidence interval for all models

colnames(results.Leave1studyout)=c("Estimate", "SE", "zval", "pval", "ci.lb", "ci.ub") # change column names 
kable(results.Leave1studyout)%>% kable_styling("striped", position="left") %>% scroll_box(width="100%", height="500px") # Table of the results from all models
Estimate SE zval pval ci.lb ci.ub
-0.3253221 0.3107507 -1.0468911 0.2956467 -0.9358339 0.2851897
-0.4037084 0.3101145 -1.3018043 0.1935803 -1.0129906 0.2055738
-0.3997524 0.3468279 -1.1525957 0.2497971 -1.0816832 0.2821784
0.0435382 0.2731984 0.1593648 0.8734478 -0.4932604 0.5803368
-0.3312637 0.3129344 -1.0585724 0.2903281 -0.9461575 0.2836301
-0.2423434 0.3010346 -0.8050351 0.4211923 -0.8338304 0.3491436
-0.3309747 0.3124412 -1.0593181 0.2899684 -0.9448401 0.2828908
-0.2229376 0.3086359 -0.7223322 0.4706194 -0.8301566 0.3842813
-0.3882687 0.3207704 -1.2104253 0.2267090 -1.0185498 0.2420125
-0.4843182 0.2868896 -1.6881692 0.0920640 -1.0481112 0.0794748
dat %>% group_by(Author_year, Study_ID) %>% summarise(mean=mean(lnRR)) # Study F005 (DelGobbo_2008) has much lower effect sizes than the others. 
## # A tibble: 10 x 3
## # Groups:   Author_year [10]
##    Author_year      Study_ID    mean
##    <chr>            <fct>      <dbl>
##  1 Alves_2017       F001     -0.0774
##  2 Barbosa_2018     F002      0.198 
##  3 Bhavsar_2014     F003      0.153 
##  4 DelGobbo_2008    F005     -2.00  
##  5 Hu_2020          F006     -0.134 
##  6 Kim_2020         F007     -0.887 
##  7 Luo_2019         F008     -0.161 
##  8 Sungur_2019      F010     -0.893 
##  9 Taylor_2019      F011      0.213 
## 10 Vassiliadou_2015 F013      0.673

Subset analysis without Study_ID F005 (Del Gobbo et al. 2008)

Cooking time

dat.sens <- filter(dat, Author_year != "DelGobbo_2008")

include <- row.names(cor_tree) %in% dat.sens$Phylogeny  # Check which rows are present in the phylogenetic tree 
cor_tree_sens <- cor_tree[include, include]  # Only include the species that match the reduced data set 

dat.sens <- as.data.frame(dat.sens)  # convert data set into a data frame to calculate VCV matrix 
VCV_lnRR.sens <- make_VCV_matrix(dat.sens, V = "var_lnRR", cluster = "Cohort_ID", 
    obs = "Effect_ID", rho = 0.5)  # create VCV matrix for the specified data

mod.sens <- rma.mv(lnRR, VCV_lnRR.sens, mods = ~Length_cooking_time_in_s, random = list(~1 | 
    Study_ID, ~1 | Phylogeny, ~1 | Species_common, ~1 | PFAS_type, ~1 | Effect_ID), 
    R = list(Phylogeny = cor_tree_sens), test = "t", data = dat.sens)
summary(mod.sens)
## 
## Multivariate Meta-Analysis Model (k = 430; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -276.6805   553.3611   567.3611   595.7749   567.6277   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.2023  0.4498      8     no        Study_ID   no 
## sigma^2.2  0.0311  0.1763     22     no       Phylogeny  yes 
## sigma^2.3  0.0112  0.1059     22     no  Species_common   no 
## sigma^2.4  0.0964  0.3105     17     no       PFAS_type   no 
## sigma^2.5  0.0683  0.2613    430     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 428) = 1249.4809, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 428) = 83.0392, p-val < .0001
## 
## Model Results:
## 
##                           estimate      se     tval    pval    ci.lb    ci.ub 
## intrcpt                     0.6929  0.2349   2.9499  0.0034   0.2312   1.1546 
## Length_cooking_time_in_s   -0.0012  0.0001  -9.1126  <.0001  -0.0015  -0.0009 
##  
## intrcpt                    ** 
## Length_cooking_time_in_s  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
dat.time.sens <- filter(dat.sens, Length_cooking_time_in_s != "NA")
plot_continuous(dat.time.sens, mod.sens, dat.time.sens$Length_cooking_time_in_s, 
    "Cooking time (s)")  # The relationship with cooking time appears even stronger

Effect of cooking time on lnRR for each cooking category
oil_dat.sens <- filter(dat.sens, Cooking_Category == "oil-based")
water_dat.sens <- filter(dat.sens, Cooking_Category == "water-based")
dry_dat.sens <- filter(dat.sens, Cooking_Category == "No liquid")


oil_dat_time.sens <- filter(oil_dat.sens, Length_cooking_time_in_s != "NA")
water_dat_time.sens <- filter(water_dat.sens, Length_cooking_time_in_s != "NA")
dry_dat_time.sens <- filter(dry_dat.sens, Length_cooking_time_in_s != "NA")

model_oil_time.sens <- run_model_oil(oil_dat_time.sens, ~Length_cooking_time_in_s)
model_water_time.sens <- run_model_water(water_dat_time.sens, ~Length_cooking_time_in_s)
model_dry_time.sens <- run_model_dry(dry_dat_time.sens, ~Length_cooking_time_in_s)

pred_oil_time.sens <- predict.rma(model_oil_time.sens)
pred_water_time.sens <- predict.rma(model_water_time.sens)
pred_dry_time.sens <- predict.rma(model_dry_time.sens)

oil_dat_time.sens <- mutate(oil_dat_time.sens, ci.lb = pred_oil_time.sens$ci.lb, 
    ci.ub = pred_oil_time.sens$ci.ub, fit = pred_oil_time.sens$pred)

water_dat_time.sens <- mutate(water_dat_time.sens, ci.lb = pred_water_time.sens$ci.lb, 
    ci.ub = pred_water_time.sens$ci.ub, fit = pred_water_time.sens$pred)

dry_dat_time.sens <- mutate(dry_dat_time.sens, ci.lb = pred_dry_time.sens$ci.lb, 
    ci.ub = pred_dry_time.sens$ci.ub, fit = pred_dry_time.sens$pred)

# Actual plot

ggplot(dat.sens, aes(x = Length_cooking_time_in_s, y = lnRR, fill = Cooking_Category)) + 
    
geom_ribbon(data = water_dat_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), 
    alpha = 0.2) + geom_line(data = water_dat_time.sens, aes(y = fit), size = 1.5, 
    col = "dodgerblue") + 
geom_ribbon(data = oil_dat_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), 
    alpha = 0.3) + geom_line(data = oil_dat_time.sens, aes(y = fit), size = 1.5, 
    col = "goldenrod") + 
geom_ribbon(data = dry_dat_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), 
    alpha = 0.25) + geom_line(data = dry_dat_time.sens, aes(y = fit), size = 1.5, 
    col = "palegreen3") + 

geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) + 
    scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "Cooking time (s)", 
    y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) + 
    theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) + 
    theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14), 
        legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(), 
        legend.direction = "horizontal", legend.title = element_text(size = 15), 
        panel.border = element_rect(colour = "black", fill = NA, size = 1.2))

###### Predictions with the full model

##### Oil based
full_model_oil_time.sens<- run_model_oil(oil_dat.sens, ~ scale(Temperature_in_Celsius) +
                                           Length_cooking_time_in_s+
                                           scale(PFAS_carbon_chain) +
                                           scale(log(Volume_liquid_ml)))

pred_oil_time.sens<-predict.rma(full_model_oil_time.sens, addx=TRUE, newmods=cbind(0,c(120:1500), 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time.sens<-as.data.frame(pred_oil_time.sens)
pred_oil_time.sens$Length_cooking_time_in_s=pred_oil_time.sens$X.Length_cooking_time_in_s
pred_oil_time.sens<-left_join(oil_dat.sens, pred_oil_time.sens, by="Length_cooking_time_in_s")


##### Water based
full_model_water_time.sens<- run_model_water(water_dat.sens, ~ scale(Temperature_in_Celsius) +
                                           Length_cooking_time_in_s+
                                           scale(PFAS_carbon_chain) +
                                           scale(log(Volume_liquid_ml)))

pred_water_time.sens<-predict.rma(full_model_water_time.sens, addx=TRUE, newmods=cbind(c(120:1500), 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time.sens<-as.data.frame(pred_water_time.sens)
pred_water_time.sens$Length_cooking_time_in_s=pred_water_time.sens$X.Length_cooking_time_in_s
pred_water_time.sens<-left_join(water_dat, pred_water_time.sens, by="Length_cooking_time_in_s")

##### No liquid 

full_model_dry_time.sens<- run_model_dry(dry_dat.sens, ~ Length_cooking_time_in_s)

pred_dry_time.sens<-predict.rma(full_model_dry_time.sens, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time.sens<-as.data.frame(pred_dry_time.sens)
pred_dry_time.sens$Length_cooking_time_in_s=pred_dry_time.sens$X.Length_cooking_time_in_s
pred_dry_time.sens<-left_join(dry_dat.sens, pred_dry_time.sens, by="Length_cooking_time_in_s")




ggplot(dat.sens,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
  
       geom_ribbon(data=pred_water_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_water_time.sens,aes(y = pred), size = 1.5, col="dodgerblue")+  
  
       geom_ribbon(data=pred_oil_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
       geom_line(data=pred_oil_time.sens,aes(y = pred), size = 1.5, col="goldenrod")+  
  
        geom_ribbon(data=pred_dry_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_dry_time.sens,aes(y = pred), size = 1.5, col="palegreen3")+  
  
  
       geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
       scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
       labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position=c(0,0), 
          legend.justification = c(0,0),
          legend.background = element_blank(), 
          legend.direction="horizontal",
          legend.title = element_text(size=15), 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Volume of liquid

dat.sens.vol <- filter(dat.sens, Volume_liquid_ml != "NA")
include <- row.names(cor_tree) %in% dat.sens.vol$Phylogeny  # Check which rows are present in the phylogenetic tree 
cor_tree_sens.vol <- cor_tree[include, include]  # Only include the species that match the reduced data set 
VCV_lnRR.sens.vol <- make_VCV_matrix(dat.sens.vol, V = "var_lnRR", cluster = "Cohort_ID", 
    obs = "Effect_ID", rho = 0.5)  # create VCV matrix for the specified data


mod.sens.vol <- rma.mv(lnRR, VCV_lnRR.sens.vol, mods = ~log(Volume_liquid_ml), random = list(~1 | 
    Study_ID, ~1 | Phylogeny, ~1 | Species_common, ~1 | PFAS_type, ~1 | Effect_ID), 
    R = list(Phylogeny = cor_tree_sens.vol), test = "t", data = dat.sens.vol)
summary(mod.sens.vol)
## 
## Multivariate Meta-Analysis Model (k = 413; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -389.4527   778.9053   792.9053   821.0355   793.1832   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.3293  0.5739      7     no        Study_ID   no 
## sigma^2.2  0.0413  0.2031     26     no       Phylogeny  yes 
## sigma^2.3  0.1058  0.3252     27     no  Species_common   no 
## sigma^2.4  0.1297  0.3601     18     no       PFAS_type   no 
## sigma^2.5  0.2120  0.4604    413     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 411) = 3390.2773, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 411) = 0.4459, p-val = 0.5046
## 
## Model Results:
## 
##                        estimate      se     tval    pval    ci.lb   ci.ub 
## intrcpt                 -0.3392  0.4516  -0.7511  0.4530  -1.2268  0.5485    
## log(Volume_liquid_ml)    0.0462  0.0691   0.6678  0.5046  -0.0897  0.1821    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot_continuous(dat.sens.vol, mod.sens.vol, log(dat.sens.vol$Volume_liquid_ml), "ln(Volume of liquid (mL))")  # The relationship with cooking time appears even stronger

Effect of cooking time on lnRR for each cooking category

The effect of volume of liquid is entirely driven by one study!!

oil_dat.sens <- filter(dat.sens, Cooking_Category == "oil-based")
water_dat.sens <- filter(dat.sens, Cooking_Category == "water-based")
dry_dat.sens <- filter(dat.sens, Cooking_Category == "No liquid")


oil_dat_vol.sens <- filter(oil_dat.sens, Volume_liquid_ml != "NA")
water_dat_vol.sens <- filter(water_dat.sens, Volume_liquid_ml != "NA")
dry_dat_vol.sens <- filter(dry_dat.sens, Volume_liquid_ml != "NA")

model_oil_vol.sens <- run_model_oil(oil_dat_vol.sens, ~log(Volume_liquid_ml))
model_water_vol.sens <- run_model_water(water_dat_vol.sens, ~log(Volume_liquid_ml))
model_dry_vol.sens <- run_model_dry(dry_dat_vol.sens, ~log(Volume_liquid_ml))

pred_oil_vol.sens <- predict.rma(model_oil_vol.sens)
pred_water_vol.sens <- predict.rma(model_water_vol.sens)
pred_dry_vol.sens <- predict.rma(model_dry_vol.sens)

oil_dat_vol.sens <- mutate(oil_dat_vol.sens, ci.lb = pred_oil_vol.sens$ci.lb, ci.ub = pred_oil_vol.sens$ci.ub, 
    fit = pred_oil_vol.sens$pred)

water_dat_vol.sens <- mutate(water_dat_vol.sens, ci.lb = pred_water_vol.sens$ci.lb, 
    ci.ub = pred_water_vol.sens$ci.ub, fit = pred_water_vol.sens$pred)

dry_dat_vol.sens <- mutate(dry_dat_vol.sens, ci.lb = pred_dry_vol.sens$ci.lb, ci.ub = pred_dry_vol.sens$ci.ub, 
    fit = pred_dry_vol.sens$pred)

# Actual plot

ggplot(dat.sens, aes(x = log(Volume_liquid_ml), y = lnRR, fill = Cooking_Category)) + 
    
geom_ribbon(data = water_dat_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), 
    alpha = 0.2) + geom_line(data = water_dat_vol.sens, aes(y = fit), size = 1.5, 
    col = "dodgerblue") + 
geom_ribbon(data = oil_dat_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), 
    alpha = 0.3) + geom_line(data = oil_dat_vol.sens, aes(y = fit), size = 1.5, col = "goldenrod") + 
    
geom_ribbon(data = dry_dat_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), 
    alpha = 0.2) + geom_line(data = dry_dat_vol.sens, aes(y = fit), size = 1.5, col = "palegreen3") + 
    

geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) + 
    scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Volume of liquid (mL))", 
    y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) + 
    theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) + 
    theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14), 
        legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(), 
        legend.direction = "horizontal", legend.title = element_text(size = 15), 
        panel.border = element_rect(colour = "black", fill = NA, size = 1.2))

Predictions with the full model
##### Oil based
full_model_oil_vol.sens <- run_model_oil(oil_dat.sens, ~scale(Temperature_in_Celsius) + 
    scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + log_Volume_liquid_ml)
pred_oil_vol.sens <- predict.rma(full_model_oil_vol.sens, addx = TRUE, newmods = cbind(0, 
    0, 0, c(log(5):log(750))))  # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time

pred_oil_vol.sens <- as.data.frame(pred_oil_vol.sens)
pred_oil_vol.sens <- pred_oil_vol.sens %>% mutate(Volume_liquid_ml = exp(X.log_Volume_liquid_ml), 
    Cooking_Category = "oil-based", lnRR = 0)  # for the plot to work, we need to add a column with cooking category and a column with lnRR


##### Water based

full_model_water_vol.sens <- run_model_water(water_dat.sens, ~scale(Temperature_in_Celsius) + 
    scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + log_Volume_liquid_ml)

pred_water_vol.sens <- predict.rma(full_model_water_vol.sens, addx = TRUE, newmods = cbind(0, 
    0, c(5.521461:7.824046)))  # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time

pred_water_vol.sens <- as.data.frame(pred_water_vol.sens)
pred_water_vol.sens <- pred_water_vol.sens %>% mutate(Volume_liquid_ml = exp(X.log_Volume_liquid_ml), 
    Cooking_Category = "water-based", lnRR = 0)



ggplot(dat.sens, aes(x = log(Volume_liquid_ml), y = lnRR, fill = Cooking_Category)) + 
    
geom_ribbon(data = pred_water_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), 
    alpha = 0.2) + geom_line(data = pred_water_vol.sens, aes(y = pred), size = 1.5, 
    col = "dodgerblue") + 
geom_ribbon(data = pred_oil_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), 
    alpha = 0.3) + geom_line(data = pred_oil_vol.sens, aes(y = pred), size = 1.5, 
    col = "goldenrod") + 

geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) + 
    scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Volume of liquid (mL))", 
    y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) + 
    theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) + 
    theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14), 
        legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(), 
        legend.direction = "horizontal", legend.title = element_text(size = 15), 
        panel.border = element_rect(colour = "black", fill = NA, size = 1.2))  #### The line doesn't go all the way down (the predict function doesn't capture the biggest values)

PFAS carbon chain

dat.sens.PFAS <- filter(dat.sens, PFAS_carbon_chain != "NA")
include <- row.names(cor_tree) %in% dat.sens.PFAS$Phylogeny  # Check which rows are present in the phylogenetic tree 
cor_tree_sens.PFAS <- cor_tree[include, include]  # Only include the species that match the reduced data set 
VCV_lnRR.sens.PFAS <- make_VCV_matrix(dat.sens.PFAS, V = "var_lnRR", cluster = "Cohort_ID", 
    obs = "Effect_ID", rho = 0.5)  # create VCV matrix for the specified data


mod.sens.PFAS <- rma.mv(lnRR, VCV_lnRR.sens.PFAS, mods = ~PFAS_carbon_chain, random = list(~1 | 
    Study_ID, ~1 | Phylogeny, ~1 | Species_common, ~1 | PFAS_type, ~1 | Effect_ID), 
    R = list(Phylogeny = cor_tree_sens.PFAS), test = "t", data = dat.sens.PFAS)
summary(mod.sens.PFAS)
## 
## Multivariate Meta-Analysis Model (k = 486; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -464.4535   928.9070   942.9070   972.1816   943.1423   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.2361  0.4859      9     no        Study_ID   no 
## sigma^2.2  0.0998  0.3159     30     no       Phylogeny  yes 
## sigma^2.3  0.1036  0.3218     31     no  Species_common   no 
## sigma^2.4  0.0968  0.3111     18     no       PFAS_type   no 
## sigma^2.5  0.2299  0.4795    486     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 484) = 4439.7079, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 484) = 1.1682, p-val = 0.2803
## 
## Model Results:
## 
##                    estimate      se     tval    pval    ci.lb   ci.ub 
## intrcpt             -0.2244  0.3706  -0.6054  0.5452  -0.9525  0.5038    
## PFAS_carbon_chain    0.0301  0.0278   1.0809  0.2803  -0.0246  0.0847    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot_continuous(dat.sens.PFAS, mod.sens.PFAS, dat.sens.PFAS$PFAS_carbon_chain, "PFAS carbon chain length")  # The relationship with cooking time appears even stronger

Effect of carbon chain length on lnRR for each cooking category
oil_dat.sens <- filter(dat.sens, Cooking_Category == "oil-based")
water_dat.sens <- filter(dat.sens, Cooking_Category == "water-based")
dry_dat.sens <- filter(dat.sens, Cooking_Category == "No liquid")


oil_dat_PFAS.sens <- filter(oil_dat.sens, PFAS_carbon_chain != "NA")
water_dat_PFAS.sens <- filter(water_dat.sens, PFAS_carbon_chain != "NA")
dry_dat_PFAS.sens <- filter(dry_dat.sens, PFAS_carbon_chain != "NA")

model_oil_PFAS.sens <- run_model_oil(oil_dat_PFAS.sens, ~PFAS_carbon_chain)
model_water_PFAS.sens <- run_model_water(water_dat_PFAS.sens, ~PFAS_carbon_chain)
model_dry_PFAS.sens <- run_model_dry(dry_dat_PFAS.sens, ~PFAS_carbon_chain)

pred_oil_PFAS.sens <- predict.rma(model_oil_PFAS.sens)
pred_water_PFAS.sens <- predict.rma(model_water_PFAS.sens)
pred_dry_PFAS.sens <- predict.rma(model_dry_PFAS.sens)

oil_dat_PFAS.sens <- mutate(oil_dat_PFAS.sens, ci.lb = pred_oil_PFAS.sens$ci.lb, 
    ci.ub = pred_oil_PFAS.sens$ci.ub, fit = pred_oil_PFAS.sens$pred)

water_dat_PFAS.sens <- mutate(water_dat_PFAS.sens, ci.lb = pred_water_PFAS.sens$ci.lb, 
    ci.ub = pred_water_PFAS.sens$ci.ub, fit = pred_water_PFAS.sens$pred)

dry_dat_PFAS.sens <- mutate(dry_dat_PFAS.sens, ci.lb = pred_dry_PFAS.sens$ci.lb, 
    ci.ub = pred_dry_PFAS.sens$ci.ub, fit = pred_dry_PFAS.sens$pred)

# Actual plot

ggplot(dat.sens, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) + 
    
geom_ribbon(data = dry_dat_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), 
    alpha = 0.2) + geom_line(data = dry_dat_PFAS.sens, aes(y = fit), size = 1.5, 
    col = "palegreen3") + 
geom_ribbon(data = oil_dat_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), 
    alpha = 0.3) + geom_line(data = oil_dat_PFAS.sens, aes(y = fit), size = 1.5, 
    col = "goldenrod") + 
geom_ribbon(data = water_dat_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), 
    alpha = 0.3) + geom_line(data = water_dat_PFAS.sens, aes(y = fit), size = 1.5, 
    col = "dodgerblue") + 
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) + 
    scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "PFAS carbon chain length", 
    y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) + 
    theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) + 
    theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14), 
        legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(), 
        legend.direction = "horizontal", legend.title = element_text(size = 15), 
        panel.border = element_rect(colour = "black", fill = NA, size = 1.2))

Predictions with the full model
##### Oil based
full_model_oil_PFAS.sens<- run_model_oil(oil_dat.sens, ~ scale(Temperature_in_Celsius) +
                                           scale(Length_cooking_time_in_s)+
                                           PFAS_carbon_chain +
                                           scale(log(Volume_liquid_ml)))
pred_oil_PFAS.sens<-predict.rma(full_model_oil_PFAS.sens, addx=TRUE, newmods=cbind(0,0, c(3:14),0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS.sens<-as.data.frame(pred_oil_PFAS.sens)
pred_oil_PFAS.sens$PFAS_carbon_chain=pred_oil_PFAS.sens$X.PFAS_carbon_chain
pred_oil_PFAS.sens<-left_join(oil_dat.sens, pred_oil_PFAS.sens, by="PFAS_carbon_chain")


##### Water based
full_model_water_PFAS.sens<- run_model_water(water_dat.sens, ~ scale(Temperature_in_Celsius) +
                                           scale(Length_cooking_time_in_s)+
                                           PFAS_carbon_chain +
                                           scale(log(Volume_liquid_ml)))

pred_water_PFAS.sens<-predict.rma(full_model_water_PFAS.sens, addx=TRUE, newmods=cbind(0, c(3:14),0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS.sens<-as.data.frame(pred_water_PFAS.sens)
pred_water_PFAS.sens$PFAS_carbon_chain=pred_water_PFAS.sens$X.PFAS_carbon_chain
pred_water_PFAS.sens<-left_join(water_dat.sens, pred_water_PFAS.sens, by="PFAS_carbon_chain")

##### No liquid 

full_model_dry_PFAS.sens<- run_model_dry(dry_dat.sens, ~ PFAS_carbon_chain)

pred_dry_PFAS.sens<-predict.rma(full_model_dry_PFAS.sens, addx=TRUE)
pred_dry_PFAS.sens<-as.data.frame(pred_dry_PFAS.sens)
pred_dry_PFAS.sens$PFAS_carbon_chain=pred_dry_PFAS.sens$X.PFAS_carbon_chain
pred_dry_PFAS.sens<-left_join(dry_dat.sens, pred_dry_PFAS.sens, by="PFAS_carbon_chain")



ggplot(dat.sens,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
  
    
       geom_ribbon(data=pred_dry_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_dry_PFAS.sens,aes(y = pred), size = 1.5, col="palegreen3")+  
  
  
       geom_ribbon(data=pred_water_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_water_PFAS.sens,aes(y = pred), size = 1.5, col="dodgerblue")+  
  
  
       geom_ribbon(data=pred_oil_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
       geom_line(data=pred_oil_PFAS.sens,aes(y = pred), size = 1.5, col="goldenrod")+  
  
  
       geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
       scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
       labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position=c(0,0), 
          legend.justification = c(0,0),
          legend.background = element_blank(), 
          legend.direction="horizontal",
          legend.title = element_text(size=15), 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Funnel plot

full_mod.sens<-run_model(dat.sens, ~ -1 +  Cooking_Category  + 
                                scale(Temperature_in_Celsius) + 
                                scale(Length_cooking_time_in_s) + 
                                scale(PFAS_carbon_chain) + 
                                scale(log(Volume_liquid_ml)))


funnel(full_mod.sens, yaxis="seinv")

Figures for publication

Figure 2

run_model(dat, ~-1 + Cooking_Category + I(sqrt(1/N_tilde)) + scale(Publication_year) + scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + scale(log(Volume_liquid_ml)))

Cooking time

full_model_time<- run_model(dat, ~     scale(Temperature_in_Celsius) +
                                       Length_cooking_time_in_s+
                                       scale(PFAS_carbon_chain) +
                                       scale(log(Volume_liquid_ml)))

pred_full_model_time<-predict.rma(full_model_time, addx=TRUE, newmods=cbind(0,c(120:1500), 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_time<-as.data.frame(pred_full_model_time)
pred_full_model_time$Length_cooking_time_in_s=pred_full_model_time$X.Length_cooking_time_in_s
pred_full_model_time<-left_join(dat, pred_full_model_time, by="Length_cooking_time_in_s")



uni_model_time<- run_model(dat, ~ Length_cooking_time_in_s)

pred_uni_model_time<-predict.rma(uni_model_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_time<-as.data.frame(pred_uni_model_time)
pred_uni_model_time$Length_cooking_time_in_s=pred_uni_model_time$X.Length_cooking_time_in_s
pred_uni_model_time<-left_join(dat, pred_uni_model_time, by="Length_cooking_time_in_s")



p_time<-ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR)) +
  
       geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
  
       geom_ribbon(data=pred_full_model_time, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
       geom_line(data=pred_full_model_time,aes(y = pred), size = 1.5, color="orangered2")+  
  
       geom_ribbon(data=pred_uni_model_time, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
       geom_line(data=pred_uni_model_time,aes(y = pred), size = 1.5, col="gray30")+  
  
       labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.position="none",
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Volume of liquid

full_model_vol<- run_model(dat, ~      scale(Temperature_in_Celsius) +
                                       scale(Length_cooking_time_in_s)+
                                       scale(PFAS_carbon_chain) +
                                       log_Volume_liquid_ml)

pred_full_model_vol<-predict.rma(full_model_vol, addx=TRUE, newmods=cbind(0,0, 0, c(log(5):log(59777))))
pred_full_model_vol<-as.data.frame(pred_full_model_vol)
pred_full_model_vol$log_Volume_liquid_ml=pred_full_model_vol$X.log_Volume_liquid_ml
pred_full_model_vol<- pred_full_model_vol %>% mutate(Volume_liquid_ml = exp(X.log_Volume_liquid_ml), lnRR = 0) 



uni_model_vol<- run_model(dat, ~ log_Volume_liquid_ml)

pred_uni_model_vol<-predict.rma(uni_model_vol, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_vol<-as.data.frame(pred_uni_model_vol)
pred_uni_model_vol$log_Volume_liquid_ml=pred_uni_model_vol$X.log_Volume_liquid_ml
pred_uni_model_vol<- pred_uni_model_vol %>% mutate(Volume_liquid_ml = exp(X.log_Volume_liquid_ml), lnRR = 0) 



p_vol<-ggplot(dat,aes(x = log_Volume_liquid_ml, y = lnRR)) +
  
       geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
  
       geom_ribbon(data=pred_full_model_vol, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
       geom_line(data=pred_full_model_vol,aes(y = pred), size = 1.5, color="orangered2")+  
  
       geom_ribbon(data=pred_uni_model_vol, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
       geom_line(data=pred_uni_model_vol,aes(y = pred), size = 1.5, col="gray30")+  
  
       labs(x = "ln[Volume of liquid (mL)]", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.position="none", 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Cooking temperature

full_model_temp<- run_model(dat, ~     Temperature_in_Celsius +
                                       scale(Length_cooking_time_in_s)+
                                       scale(PFAS_carbon_chain) +
                                       scale(log(Volume_liquid_ml)))

pred_full_model_temp<-predict.rma(full_model_temp, addx=TRUE, newmods=cbind(c(75:300),0, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_temp<-as.data.frame(pred_full_model_temp)
pred_full_model_temp$Temperature_in_Celsius=pred_full_model_temp$X.Temperature_in_Celsius
pred_full_model_temp<-left_join(dat, pred_full_model_temp, by="Temperature_in_Celsius")



uni_model_temp<- run_model(dat, ~ Temperature_in_Celsius)

pred_uni_model_temp<-predict.rma(uni_model_temp, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_temp<-as.data.frame(pred_uni_model_temp)
pred_uni_model_temp$Temperature_in_Celsius=pred_uni_model_temp$X.Temperature_in_Celsius
pred_uni_model_temp<-left_join(dat, pred_uni_model_temp, by="Temperature_in_Celsius")



p_temp<-ggplot(dat,aes(x = Temperature_in_Celsius, y = lnRR)) +
  
       geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
  
       geom_ribbon(data=pred_full_model_temp, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
       geom_line(data=pred_full_model_temp,aes(y = pred), size = 1.5, color="orangered2")+  
  
       geom_ribbon(data=pred_uni_model_temp, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
       geom_line(data=pred_uni_model_temp,aes(y = pred), size = 1.5, col="gray30")+  
  
       labs(x = "Cooking temperature (°C)", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position=c(1,0), 
          legend.justification = c(1,0),
          legend.background = element_blank(), 
          legend.direction="vertical",
          legend.title = element_text(size=15), 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

PFAS carbon chain length

full_model_PFAS<- run_model(dat, ~     scale(Temperature_in_Celsius) +
                                       scale(Length_cooking_time_in_s)+
                                       PFAS_carbon_chain +
                                       scale(log(Volume_liquid_ml)))

pred_full_model_PFAS<-predict.rma(full_model_PFAS, addx=TRUE, newmods=cbind(0, 0, c(3:14), 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_PFAS<-as.data.frame(pred_full_model_PFAS)
pred_full_model_PFAS$PFAS_carbon_chain=pred_full_model_PFAS$X.PFAS_carbon_chain
pred_full_model_PFAS<-left_join(dat, pred_full_model_PFAS, by="PFAS_carbon_chain")



uni_model_PFAS<- run_model(dat, ~ PFAS_carbon_chain)

pred_uni_model_PFAS<-predict.rma(uni_model_PFAS, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_PFAS<-as.data.frame(pred_uni_model_PFAS)
pred_uni_model_PFAS$PFAS_carbon_chain=pred_uni_model_PFAS$X.PFAS_carbon_chain
pred_uni_model_PFAS<-left_join(dat, pred_uni_model_PFAS, by="PFAS_carbon_chain")



p_PFAS<-ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR)) +
  
       geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
  
       geom_ribbon(data=pred_full_model_PFAS, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
       geom_line(data=pred_full_model_PFAS,aes(y = pred), size = 1.5, color="orangered2")+  
  
       geom_ribbon(data=pred_uni_model_PFAS, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
       geom_line(data=pred_uni_model_PFAS,aes(y = pred), size = 1.5, col="gray30")+  
  
       labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.position="none",
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Combine plots and save

(p_time + p_vol)/(p_temp + p_PFAS) + plot_annotation(tag_levels = c("A", "B", "C", 
    "D"))

ggsave("fig/Fig_2.png", width = 15, height = 12, dpi = 1200)

Figure 3

Adapt orchard_plot function

my_orchard<-function (object, mod = "Int", xlab, N = "none", 
    alpha = 0.5, angle = 90, cb = FALSE, k = TRUE, transfm = c("none", 
        "tanh"), condition.lab = "Condition") 
{
    transfm <- match.arg(transfm)
    if (any(class(object) %in% c("rma.mv", "rma"))) {
        if (mod != "Int") {
            object <- mod_results(object, mod)
        }
        else {
            object <- mod_results(object, mod = "Int")
        }
    }
    mod_table <- object$mod_table
    data <- object$data
    data$moderator <- factor(data$moderator, levels = mod_table$name, 
        labels = mod_table$name)
    data$scale <- (1/sqrt(data[, "vi"]))
    legend <- "Precision (1/SE)"
    if (any(N != "none")) {
        data$scale <- N
        legend <- "Sample Size (N)"
    }
    if (transfm == "tanh") {
        cols <- sapply(mod_table, is.numeric)
        mod_table[, cols] <- Zr_to_r(mod_table[, cols])
        data$yi <- Zr_to_r(data$yi)
        label <- xlab
    }
    else {
        label <- xlab
    }
    mod_table$K <- as.vector(by(data, data[, "moderator"], 
        function(x) length(x[, "yi"])))
    group_no <- length(unique(mod_table[, "name"]))
    cbpl <- c("#55C667FF", "goldenrod2", "dodgerblue3") # change colors
    if (names(mod_table)[2] == "condition") {
        condition_no <- length(unique(mod_table[, "condition"]))
        plot <- ggplot2::ggplot() + ggbeeswarm::geom_quasirandom(data = data, 
            ggplot2::aes(y = yi, x = moderator, size = scale, 
                color = moderator), alpha = alpha) + ggplot2::geom_hline(yintercept = 0, 
            linetype = 2, colour = "black", alpha = alpha) + 
            ggplot2::geom_linerange(data = mod_table, ggplot2::aes(x = name, 
                ymin = lowerPR, ymax = upperPR), size = 0.75, # change size confidence intervals and swap CL with PR. Added whiskers
                position = ggplot2::position_dodge2(width = 0.3)) + 
            ggplot2::geom_pointrange(data = mod_table, ggplot2::aes(y = estimate, 
                x = name, ymin = lowerCL, ymax = upperCL, shape = as.factor(condition), # swap CL with PR
                fill = name), size = 1.6, stroke=2.2, width= 1.3, position = ggplot2::position_dodge2(width = 0.3)) + # change size point and prediction intervals
            ggplot2::scale_shape_manual(values = 20 + (1:condition_no)) + 
            ggplot2::coord_flip() + ggplot2::theme_bw() + ggplot2::guides(fill = "none", 
            colour = "none") + ggplot2::theme(legend.position = c(0, 
            1), legend.justification = c(0, 1)) + ggplot2::theme(legend.title = ggplot2::element_text(size = 9)) + 
            ggplot2::theme(legend.direction = "horizontal") + 
            ggplot2::theme(legend.background = ggplot2::element_blank()) + 
            ggplot2::labs(y = label, x = "", size = legend) + 
            ggplot2::labs(shape = condition.lab) + ggplot2::theme(axis.text.y = ggplot2::element_text(size = 10, 
            colour = "black", hjust = 0.5, angle = angle))
        plot <- plot + ggplot2::annotate("text", y = (max(data$yi) + 
            (max(data$yi) * 0.1)), x = (seq(1, group_no, 1) + 
            0.3), label = paste("italic(k)==", mod_table$K[1:group_no]), 
            parse = TRUE, hjust = "right", size = 3.5)
    }
    else {
        plot <- ggplot2::ggplot(data = mod_table, ggplot2::aes(x = estimate, 
            y = name)) + ggbeeswarm::geom_quasirandom(data = data, 
            ggplot2::aes(x = yi, y = moderator, size = scale, 
                colour = moderator), groupOnX = FALSE, alpha = alpha) + 
            ggplot2::geom_errorbarh(ggplot2::aes(xmin = lowerPR, 
                xmax = upperPR), height = 0, show.legend = FALSE, # change error barrs
                size = 0.75, alpha = 0.5) + ggplot2::geom_errorbarh(ggplot2::aes(xmin = lowerCL, 
            xmax = upperCL), height = 0.1, show.legend = FALSE, 
            size = 1.75) + ggplot2::geom_vline(xintercept = 0, 
            linetype = 2, colour = "black", alpha = alpha) + 
            ggplot2::geom_point(ggplot2::aes(fill = name), size = 8,  # change point size
                shape = 21) + ggplot2::theme_bw() + ggplot2::guides(fill = "none", 
            colour = "none") + ggplot2::theme(legend.position = c(1, 
            0), legend.justification = c(1, 0)) + ggplot2::theme(legend.title = ggplot2::element_text(size = 9)) + 
            ggplot2::theme(legend.direction = "horizontal") + 
            ggplot2::theme(legend.background = ggplot2::element_blank()) + 
            ggplot2::labs(x = label, y = "", size = legend) + 
            ggplot2::theme(axis.text.y = ggplot2::element_text(size = 10, 
                colour = "black", hjust = 0.5, angle = angle))
        if (k == TRUE) {
            plot <- plot + ggplot2::annotate("text", x = (max(data$yi) + 
                (max(data$yi) * 0.1)), y = (seq(1, group_no, 
                1) + 0.3), label = paste("italic(k)==", 
                mod_table$K), parse = TRUE, hjust = "right", 
                size = 3.5)
        }
    }
    if (cb == TRUE) {
        plot <- plot + ggplot2::scale_fill_manual(values = cbpl) + 
            ggplot2::scale_colour_manual(values = cbpl)
    }
    return(plot)
}

Run full models in original units

full_model_org_units <- run_model(dat, ~-1 + Cooking_Category + Temperature_in_Celsius + 
    Length_cooking_time_in_s + PFAS_carbon_chain + log_Volume_liquid_ml)

Figure 3A

Estimates at cooking times of 2, 10 and 25 min

time_mm <-marginal_means(full_model_org_units, data = dat, mod = "1", at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
 
p_time_mm<-my_orchard(time_mm, xlab = "lnRR", condition.lab = "Cooking time (sec)", alpha=0.3)+
           scale_size_continuous(range = c(1, 10))+
           scale_fill_manual(values="sienna2")+
           scale_colour_manual(values = "sienna2")+ # change colours
           theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
                 text = element_text(size = 24), # change font sizes
                 legend.title = element_text(size = 13),
                 legend.text = element_text(size = 10),
                 legend.position = c(0,0.28))

Figure 3B

Estimates at volumes of liquid of ~10, 500, and 10000 mL

volume_mm <-marginal_means(full_model_org_units, data = dat, mod = "1", at = list(log_Volume_liquid_ml= c(2.3, 6.2, 9.2)), by = "log_Volume_liquid_ml")
 
p_volume_mm<-my_orchard(volume_mm, xlab = "lnRR", condition.lab = "ln(liquid volume (mL))", alpha=0.3)+
           scale_size_continuous(range = c(1, 10))+
           scale_fill_manual(values="deepskyblue2")+
           scale_colour_manual(values = "deepskyblue2")+ # change colours
           theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
                 text = element_text(size = 24), # change font sizes
                 legend.title = element_text(size = 13),
                 legend.text = element_text(size = 10),
                 legend.position = c(0,0.28))

Figure 3C

Estimates at cooking times of 2, 10 and 25 min

time_mm_cat <- marginal_means(full_model_org_units, data = dat, mod = "Cooking_Category", at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
 
p_time_mm_cat<-my_orchard(time_mm_cat, xlab = "lnRR", condition.lab  = "Cooking time (sec)", alpha=0.3)+
           scale_size_continuous(range = c(1, 10))+
           scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
           scale_colour_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3"))+ # change colours
           theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
                 text = element_text(size = 24), # change font sizes
                 legend.title = element_text(size = 13),
                 legend.text = element_text(size = 10),
                 legend.position = c(0,0.12))

Combine plots and save

((p_time_mm/p_volume_mm) | p_time_mm_cat) + plot_annotation(tag_levels = c("A", "B", 
    "C"))

ggsave("fig/Fig_3.png", width = 14, height = 10, dpi = 1200)

Figure 4

Figure 4A

##### Oil based
full_model_oil_time<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
                                           Length_cooking_time_in_s+
                                           scale(PFAS_carbon_chain) +
                                           scale(log(Volume_liquid_ml)))

pred_oil_time<-predict.rma(full_model_oil_time, addx=TRUE, newmods=cbind(0,c(120:1500), 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time<-as.data.frame(pred_oil_time)
pred_oil_time$Length_cooking_time_in_s=pred_oil_time$X.Length_cooking_time_in_s
pred_oil_time<-left_join(oil_dat, pred_oil_time, by="Length_cooking_time_in_s")


##### Water based
full_model_water_time<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
                                           Length_cooking_time_in_s+
                                           scale(PFAS_carbon_chain) +
                                           scale(log(Volume_liquid_ml)))

pred_water_time<-predict.rma(full_model_water_time, addx=TRUE, newmods=cbind(c(120:1500), 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time<-as.data.frame(pred_water_time)
pred_water_time$Length_cooking_time_in_s=pred_water_time$X.Length_cooking_time_in_s
pred_water_time<-left_join(water_dat, pred_water_time, by="Length_cooking_time_in_s")

##### No liquid 

full_model_dry_time<- run_model_dry(dry_dat, ~ Length_cooking_time_in_s)

pred_dry_time<-predict.rma(full_model_dry_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time<-as.data.frame(pred_dry_time)
pred_dry_time$Length_cooking_time_in_s=pred_dry_time$X.Length_cooking_time_in_s
pred_dry_time<-left_join(dry_dat, pred_dry_time, by="Length_cooking_time_in_s")




p_4A<-ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
  
       geom_ribbon(data=pred_water_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_water_time,aes(y = pred), size = 1.5, col="dodgerblue")+  
  
       geom_ribbon(data=pred_oil_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_oil_time,aes(y = pred), size = 1.5, col="goldenrod")+  
  
        geom_ribbon(data=pred_dry_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_dry_time,aes(y = pred), size = 1.5, col="palegreen3")+  
  
  
       geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
       scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
       labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position="none",
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Figure 4B

##### Oil based
full_model_oil_vol <- run_model_oil(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) + 
    scale(PFAS_carbon_chain) + log_Volume_liquid_ml)
pred_oil_vol <- predict.rma(full_model_oil_vol, addx = TRUE, newmods = cbind(0, 0, 
    0, c(log(5):log(750))))  # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time

pred_oil_vol <- as.data.frame(pred_oil_vol)
pred_oil_vol <- pred_oil_vol %>% mutate(Volume_liquid_ml = exp(X.log_Volume_liquid_ml), 
    Cooking_Category = "oil-based", lnRR = 0)  # for the plot to work, we need to add a column with cooking category and a column with lnRR


##### Water based

full_model_water_vol <- run_model_water(water_dat, ~scale(Temperature_in_Celsius) + 
    scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + log_Volume_liquid_ml)

pred_water_vol <- predict.rma(full_model_water_vol, addx = TRUE, newmods = cbind(0, 
    0, c(log(250):log(59777))))  # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time

pred_water_vol <- as.data.frame(pred_water_vol)
pred_water_vol <- pred_water_vol %>% mutate(Volume_liquid_ml = exp(X.log_Volume_liquid_ml), 
    Cooking_Category = "water-based", lnRR = 0)



p_4B <- ggplot(dat, aes(x = log(Volume_liquid_ml), y = lnRR, fill = Cooking_Category)) + 
    
geom_ribbon(data = pred_water_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), 
    alpha = 0.2) + geom_line(data = pred_water_vol, aes(y = pred), size = 1.5, col = "dodgerblue") + 
    
geom_ribbon(data = pred_oil_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) + 
    geom_line(data = pred_oil_vol, aes(y = pred), size = 1.5, col = "goldenrod") + 
    

geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) + 
    scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Volume of liquid (mL))", 
    y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) + 
    theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) + 
    theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14), 
        legend.position = "none", panel.border = element_rect(colour = "black", fill = NA, 
            size = 1.2))  #### The line doesn't go all the way down for water-based because the highest values are not included in the full model

Figure 4C

full_model_oil_temp<- run_model_oil(oil_dat, ~ Temperature_in_Celsius +
                                           scale(Length_cooking_time_in_s)+
                                           scale(PFAS_carbon_chain) +
                                           scale(log(Volume_liquid_ml)))
pred_oil_temp<-predict.rma(full_model_oil_temp, addx=TRUE, newmods=cbind(c(75:300),0, 0,0)) 
pred_oil_temp<-as.data.frame(pred_oil_temp)
pred_oil_temp$Temperature_in_Celsius=pred_oil_temp$X.Temperature_in_Celsius
pred_oil_temp<-left_join(oil_dat, pred_oil_temp, by="Temperature_in_Celsius")



p_4C<-ggplot(dat,aes(x = Temperature_in_Celsius, y = lnRR, fill=Cooking_Category)) +
    
       geom_ribbon(data=pred_oil_temp, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
       geom_line(data=pred_oil_temp,aes(y = pred), size = 1.5, col="goldenrod")+  
  
       geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
       scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
       labs(x = "Cooking temperature (°C)", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position=c(1,0), 
          legend.justification = c(1,0),
          legend.background = element_blank(), 
          legend.direction="vertical",
          legend.title = element_text(size=15), 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Figure 4D

##### Oil based
full_model_oil_PFAS<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
                                           scale(Length_cooking_time_in_s)+
                                           PFAS_carbon_chain +
                                           scale(log(Volume_liquid_ml)))
pred_oil_PFAS<-predict.rma(full_model_oil_PFAS, addx=TRUE, newmods=cbind(0,0, c(3:14),0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS<-as.data.frame(pred_oil_PFAS)
pred_oil_PFAS$PFAS_carbon_chain=pred_oil_PFAS$X.PFAS_carbon_chain
pred_oil_PFAS<-left_join(oil_dat, pred_oil_PFAS, by="PFAS_carbon_chain")


##### Water based
full_model_water_PFAS<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
                                           scale(Length_cooking_time_in_s)+
                                           PFAS_carbon_chain +
                                           scale(log(Volume_liquid_ml)))

pred_water_PFAS<-predict.rma(full_model_water_PFAS, addx=TRUE, newmods=cbind(0, c(3:14),0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS<-as.data.frame(pred_water_PFAS)
pred_water_PFAS$PFAS_carbon_chain=pred_water_PFAS$X.PFAS_carbon_chain
pred_water_PFAS<-left_join(water_dat, pred_water_PFAS, by="PFAS_carbon_chain")

##### No liquid 

full_model_dry_PFAS<- run_model_dry(dry_dat, ~ PFAS_carbon_chain)

pred_dry_PFAS<-predict.rma(full_model_dry_PFAS, addx=TRUE)
pred_dry_PFAS<-as.data.frame(pred_dry_PFAS)
pred_dry_PFAS$PFAS_carbon_chain=pred_dry_PFAS$X.PFAS_carbon_chain
pred_dry_PFAS<-left_join(dry_dat, pred_dry_PFAS, by="PFAS_carbon_chain")




p_4D<-ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
  
    
       geom_ribbon(data=pred_dry_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_dry_PFAS,aes(y = pred), size = 1.5, col="palegreen3")+  
  
  
       geom_ribbon(data=pred_water_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_water_PFAS,aes(y = pred), size = 1.5, col="dodgerblue")+  
  
  
       geom_ribbon(data=pred_oil_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
       geom_line(data=pred_oil_PFAS,aes(y = pred), size = 1.5, col="goldenrod")+  
  
  
       geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
       scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
       labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position="none",
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Combine and save

(p_4A + p_4B)/(p_4C + p_4D) + plot_annotation(tag_levels = c("A", "B", "C", "D"))

ggsave("fig/Fig_4.png", width = 15, height = 12, dpi = 1200)

Figure 5

Figure 5A

dat$Study_ID<- as.factor(dat$Study_ID)

funnel(full_model, 
      yaxis="seinv", # Inverse of standard error (precision) as the y axis
      level = c(90, 95, 99),  # levels of statistical significance highlighted 
      shade = c("white", "gray75", "gray55", "gray40"), # shades for different levels of statistical significance
      legend = TRUE, # display legend
      ylab="Precision (1/SE)", 
      cex.lab=1.75, 
      digits=1, 
      ylim=c(1,1.2),
      cex=2,
      pch=21,
      col=dat$Study_ID)

p_5A <-funnel(full_model, 
      yaxis="seinv", # Inverse of standard error (precision) as the y axis
      level = c(90, 95, 99),  # levels of statistical significance highlighted 
      shade = c("white", "gray75", "gray55", "gray40"), # shades for different levels of statistical significance
      legend = TRUE, # display legend
      ylab="Precision (1/SE)", 
      cex.lab=1.75, 
      digits=1, 
      ylim=c(1,1.2),
      cex=2,
      pch=21,
      col=dat$Study_ID)

Figure 5B

full_model_egger <- run_model(dat, ~ - 1 +
                      I(sqrt(1/N_tilde)) +  
                      scale(Publication_year) + 
                      scale(Temperature_in_Celsius) +
                      scale(Length_cooking_time_in_s) +
                      scale(PFAS_carbon_chain) +
                      scale(log(Volume_liquid_ml))) # Model to get predictions


pred_egger<-predict.rma(full_model_egger, addx=TRUE, newmods=cbind(c(0.1825742:1.414214),0,0,0 ,0, 0)) 
pred_egger<-as.data.frame(pred_egger)
pred_egger$SE_eff_N=pred_egger$X.I.sqrt.1.N_tilde..
pred_egger<- pred_egger %>% mutate(N_tilde = ((1/X.I.sqrt.1.N_tilde..)^2), lnRR = 0) 

p_5B<-ggplot(dat,aes(x = sqrt(1/N_tilde), y = lnRR)) +
  
       geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
  
       geom_ribbon(data=pred_egger, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
       geom_line(data=pred_egger,aes(y = pred), size = 1.5, color="orangered2")+  

       labs(x = "Standard error", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.position="none",
          panel.border=element_rect(colour="black", fill=NA, size=1.2))+
  xlim(0.18,1.19)

Figure 5C

full_model_pub <- run_model(dat, ~ - 1 +
                      scale(I(sqrt(1/N_tilde))) +  
                      Publication_year + 
                      scale(Temperature_in_Celsius) +
                      scale(Length_cooking_time_in_s) +
                      scale(PFAS_carbon_chain) +
                      scale(log(Volume_liquid_ml))) # Model to get predictions


pred_pub<-predict.rma(full_model_pub, addx=TRUE, newmods=cbind(0,c(2008:2020),0,0 ,0, 0)) 
pred_pub<-as.data.frame(pred_pub)
pred_pub$Publication_year=pred_pub$X.Publication_year
pred_pub<-left_join(dat, pred_pub, by="Publication_year")



p_5C<-ggplot(dat,aes(x = Publication_year, y = lnRR)) +
  
       geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
  
       geom_ribbon(data=pred_pub, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
       geom_line(data=pred_pub,aes(y = pred), size = 1.5, color="orangered2")+  

       labs(x = "Publication year", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.position="none",
          panel.border=element_rect(colour="black", fill=NA, size=1.2))  +
   scale_x_continuous(breaks=c(2008, 2010, 2012, 2014, 2016, 2018 ,2020))

Combine and save

For some reason I cannot save the funnel plot. This will be manually copied from the Rmd

(ggdraw(p_5A) + ggdraw(p_5B) + ggdraw(p_5C))

ggsave("fig/Fig_5BC.png", width = 18, height = 7, dpi = 1200)